{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:30:48Z","timestamp":1757619048710,"version":"3.44.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Research on the Current Status and Development Models of Innovation Districts","award":["222400410013"],"award-info":[{"award-number":["222400410013"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>At a time when the global wave of informatization and rural revitalization strategies are intertwined, smart rural construction, youth innovation and entrepreneurship have become the core driving forces to promote the modernization of agriculture and rural areas. However, there are obvious shortcomings in the data prediction and decision support of smart rural construction, especially in the ability to accurately model and efficiently process complex rural environmental data. Based on this background, this study actively explores the application potential of Long short-term memory network (LSTM) and simulated annealing algorithm in the construction of smart villages and the optimization of college students\u2019 skill entrepreneurship paths. According to the actual needs of smart rural precision agriculture, the LSTM algorithm is used to predict key indicators such as crop growth cycle, probability of occurrence of pests and diseases, and price fluctuations of agricultural products in the market. The experimental results show that the accuracy of the LSTM model in crop yield prediction is as high as 92%, and the accuracy rate of price prediction is 95%, which is far beyond traditional statistical methods, and can provide farmers with effective market intelligence in a timely manner to help farmers increase production and income. At the same time, in order to improve the success rate and profitability of college students\u2019 skill entrepreneurship projects, a series of simulation experiments were carried out with the help of simulated annealing algorithm, and an optimal path taking into account costs and benefits was determined through the simulation and comparison of different entrepreneurial models. Under this path, the initial return on investment of entrepreneurial projects can reach 35%, which is significantly higher than the industry average, providing clear guidance for young entrepreneurs. In addition, the LSTM is suitable for processing time series data and can effectively analyze the development and changes of smart rural construction, while the simulated annealing algorithm can efficiently find a near-optimal solution to optimize the entrepreneurial path. Based on these two algorithms, this study enriches the knowledge in the field of smart village construction and college students\u2019 skill entrepreneurship, and is of great significance in the planning and decision-making of smart village construction and the ideas and methods of college students\u2019 entrepreneurship.<\/jats:p>","DOI":"10.1007\/s44163-025-00407-5","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T15:21:10Z","timestamp":1752852070000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Smart village construction and college students\u2019 skills entrepreneurship path based on LSTM and simulated annealing algorithm"],"prefix":"10.1007","volume":"5","author":[{"given":"Xueli","family":"Dong","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"407_CR1","first-page":"16","volume":"11","author":"H Li","year":"2021","unstructured":"Li H, Hu B, Liu Y, Yang B, Liu X, Li G et al. Classification of electricity consumption behavior based on improved K-means and LSTM. Appl Sci Basel. 2021;11:16.","journal-title":"Appl Sci Basel"},{"key":"407_CR2","doi-asserted-by":"publisher","first-page":"101933","DOI":"10.1016\/j.lmot.2023.101933","volume":"84","author":"YL Wang","year":"2023","unstructured":"Wang YL. Organic integration of ideological and political education and entrepreneurship education based on artificial neural network. Learn Motiv. 2023;84:101933.","journal-title":"Learn Motiv"},{"issue":"7","key":"407_CR3","doi-asserted-by":"publisher","first-page":"1805","DOI":"10.32604\/ee.2024.051398","volume":"121","author":"F Mukundufite","year":"2024","unstructured":"Mukundufite F, Bikorimana JMV, Lugatona AK. Smart micro grid energy system management based on optimum running cost for rural communities in Rwanda. Energy Eng. 2024;121(7):1805\u201321.","journal-title":"Energy Eng"},{"issue":"1","key":"407_CR4","doi-asserted-by":"publisher","first-page":"100124","DOI":"10.1016\/j.farsys.2024.100124","volume":"3","author":"D Sahoo","year":"2025","unstructured":"Sahoo D, Mohanty P, Mishra S, Behera MK, Mohapatra S. Does climate-smart agriculture technology improve farmers\u2019 subjective well-being? Micro-level evidence from odisha, India. Farming Syst. 2025;3(1):100124.","journal-title":"Farming Syst"},{"key":"407_CR5","doi-asserted-by":"publisher","first-page":"100666","DOI":"10.1016\/j.entcom.2024.100666","volume":"51","author":"X Zheng","year":"2024","unstructured":"Zheng X. Construction of an innovative entrepreneurship project learning platform introducing a group recommendation algorithm for college students. Entertainment Comput. 2024;51:100666.","journal-title":"Entertainment Comput"},{"key":"407_CR6","doi-asserted-by":"crossref","unstructured":"Jia H, Wang S, Ren Z. CNN-LSTM base station traffic prediction based on dual attention mechanism and timing application. Comput J, vol. 2024.","DOI":"10.1093\/comjnl\/bxae003"},{"key":"407_CR7","doi-asserted-by":"publisher","first-page":"99837","DOI":"10.1109\/ACCESS.2022.3206425","volume":"10","author":"A Halbouni","year":"2022","unstructured":"Halbouni A, Gunawan TS, Habaebi MH, Halbouni M, Kartiwi M, Ahmad R. CNN-LSTM: hybrid deep neural network for network intrusion detection system. IEEE Access. 2022;10:99837\u201349.","journal-title":"IEEE Access"},{"issue":"2","key":"407_CR8","doi-asserted-by":"publisher","first-page":"313","DOI":"10.53106\/160792642024032502013","volume":"25","author":"C Chen","year":"2024","unstructured":"Chen C, Shi SS, Peng SL. A construction of knowledge graph for semiconductor industry chain based on Lattice-LSTM and PCNN models. J Internet Technol. 2024;25(2):313\u201329.","journal-title":"J Internet Technol"},{"issue":"1","key":"407_CR9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.26480\/gwk.01.2024.13.25","volume":"8","author":"S Kai","year":"2024","unstructured":"Kai S, Shanli Y. Prediction of carbon emissions from transportation in China based on the Arima-Lstm-Bp combined model. Sci Herit J. 2024;8(1):13\u201321.","journal-title":"Sci Herit J"},{"key":"407_CR10","first-page":"6","volume":"23","author":"YJ Cao","year":"2023","unstructured":"Cao YJ, Yu XW, Jiang FL. Application of 3D image technology in rural planning. ACM Trans Asian Low Resour Lang Inform Process. 2024; 23:6.","journal-title":"ACM Trans Asian Low Resour Lang Inform Process"},{"issue":"1","key":"407_CR11","doi-asserted-by":"publisher","first-page":"100324","DOI":"10.1016\/j.jik.2023.100324","volume":"8","author":"S Adeel","year":"2023","unstructured":"Adeel S, Daniel AD, Botelho A. The effect of entrepreneurship education on the determinants of entrepreneurial behaviour among higher education students: A multi-group analysis. J Innov Knowl. 2023;8(1):100324.","journal-title":"J Innov Knowl"},{"issue":"7","key":"407_CR12","doi-asserted-by":"publisher","first-page":"8869","DOI":"10.1007\/s11063-023-11181-9","volume":"55","author":"Q Zhang","year":"2023","unstructured":"Zhang Q, Wang S, Li J. A contrastive learning framework with tree-LSTMs for aspect-based sentiment analysis. Neural Process Lett. 2023;55(7):8869\u201386.","journal-title":"Neural Process Lett"},{"issue":"2","key":"407_CR13","first-page":"4417","volume":"46","author":"Z Huang","year":"2024","unstructured":"Huang Z, Liu H, Duan C, Min J. Customer sentiment recognition in conversation based on bidirectional LSTM and self-attention mechanism. J Intell Fuzzy Syst. 2024;46(2):4417\u201328.","journal-title":"J Intell Fuzzy Syst"},{"key":"407_CR14","doi-asserted-by":"publisher","first-page":"3228","DOI":"10.1109\/ACCESS.2022.3140342","volume":"10","author":"E Ahmadzadeh","year":"2022","unstructured":"Ahmadzadeh E, Kim H, Jeong O, Kim N, Moon I. A deep bidirectional LSTM-GRU network model for automated ciphertext classification. IEEE Access. 2022;10:3228\u201337.","journal-title":"IEEE Access"},{"issue":"3","key":"407_CR15","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1109\/TII.2020.2991796","volume":"17","author":"M Ma","year":"2021","unstructured":"Ma M, Mao Z. Deep-convolution-based LSTM network for remaining useful life prediction. IEEE Trans Industr Inf. 2021;17(3):1658\u201367.","journal-title":"IEEE Trans Industr Inf"},{"issue":"2","key":"407_CR16","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1080\/03081060.2022.2164581","volume":"46","author":"D Kim","year":"2023","unstructured":"Kim D, Kim E. Development of LSTM-MLR hybrid model for radar detector missing and outlier traffic volume correction. Transp Plann Technol. 2023;46(2):182\u201399.","journal-title":"Transp Plann Technol"},{"issue":"3","key":"407_CR17","first-page":"100867","volume":"21","author":"TH Tseng","year":"2023","unstructured":"Tseng TH, Wu TY, Lian YH, Zhuang BK. Developing a value-based online learning model to predict learners\u2019 reactions to internet entrepreneurship education: the moderating role of platform type. Int J Manag Educ. 2023;21(3):100867.","journal-title":"Int J Manag Educ"},{"key":"407_CR18","doi-asserted-by":"publisher","first-page":"200073","DOI":"10.1016\/j.sasc.2024.200073","volume":"6","author":"H Wei","year":"2024","unstructured":"Wei H, Ding A, Gao Z. The application of project management methodology in the training of college students\u2019 innovation and entrepreneurship ability under sustainable education. Syst Soft Comput. 2024;6:200073.","journal-title":"Syst Soft Comput"},{"key":"407_CR19","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.sbspro.2012.11.447","volume":"69","author":"K Fekri","year":"2012","unstructured":"Fekri K, Shafiabady A, Nooranipour R, Ahghar G. Determine and compare effectiveness of entrepreneurship education based on multi- axial model and theory of constraints and compromises on learning entrepreneurship skills. Procedia Soc Behav Sci. 2012;69:566\u201370.","journal-title":"Procedia Soc Behav Sci"},{"key":"407_CR20","doi-asserted-by":"publisher","first-page":"100303","DOI":"10.1016\/j.array.2023.100303","volume":"19","author":"A Malik","year":"2023","unstructured":"Malik A, Onyema EM, Dalal S, Lilhore UK, Anand D, Sharma A, Simaiya S. Forecasting students\u2019 adaptability in online entrepreneurship education using modified ensemble machine learning model. Array. 2023;19:100303.","journal-title":"Array"},{"issue":"3","key":"407_CR21","doi-asserted-by":"publisher","first-page":"1295","DOI":"10.1007\/s00521-023-09097-5","volume":"36","author":"Y Raghuvamsi","year":"2024","unstructured":"Raghuvamsi Y, Teeparthi K. Distribution system state estimation with transformer-Bi-LSTM-based imputation model for missing measurements. Neural Comput Appl. 2024;36(3):1295\u2013312.","journal-title":"Neural Comput Appl"},{"key":"407_CR22","doi-asserted-by":"crossref","unstructured":"Zhang Y, Peng L, Ma G, Man M, Liu S. Dynamic gesture recognition model based on millimeter-wave radar with ResNet-18 and LSTM. Front Neurorobotics, 16, 2022.","DOI":"10.3389\/fnbot.2022.903197"},{"key":"407_CR23","doi-asserted-by":"crossref","unstructured":"Song HB, Yang YH, Lin J, Ye JX. An effective hyper heuristic-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem. Appl Soft Comput. 2023;135.","DOI":"10.1016\/j.asoc.2023.110022"},{"key":"407_CR24","doi-asserted-by":"crossref","unstructured":"Fathollahi-Fard AM, Wong KY, Aljuaid M. An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem. Eng Appl Artif Intell. 2023;126.","DOI":"10.1016\/j.engappai.2023.106802"},{"issue":"24","key":"407_CR25","doi-asserted-by":"publisher","first-page":"18035","DOI":"10.1007\/s00521-023-08682-y","volume":"35","author":"M Mollajafari","year":"2023","unstructured":"Mollajafari M. An efficient lightweight algorithm for scheduling tasks onto dynamically reconfigurable hardware using graph-oriented simulated annealing. Neural Comput Appl. 2023;35(24):18035\u201357.","journal-title":"Neural Comput Appl"},{"issue":"1","key":"407_CR26","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1109\/TPAMI.2022.3140886","volume":"45","author":"H Yu","year":"2023","unstructured":"Yu H, Wu SW, Dauwels J. Efficient variational Bayes learning of graphical models with smooth structural changes. IEEE Trans Pattern Anal Mach Intell. 2023;45(1):475\u201388.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"407_CR27","doi-asserted-by":"crossref","unstructured":"Baioletti M, Santini F. An encoding of argumentation problems using quadratic unconstrained binary optimization. Quantum Mach Intell. 2024;6(2).","DOI":"10.1007\/s42484-024-00186-9"},{"issue":"5","key":"407_CR28","doi-asserted-by":"publisher","first-page":"2154","DOI":"10.1007\/s12559-024-10301-4","volume":"16","author":"M Alazab","year":"2024","unstructured":"Alazab M, Khurma RA, Camacho D, Martin A. Enhanced android ransomware detection through hybrid simultaneous swarm-based optimization. Cogn Comput. 2024;16(5):2154\u201368.","journal-title":"Cogn Comput"},{"issue":"4","key":"407_CR29","doi-asserted-by":"publisher","first-page":"1305","DOI":"10.18280\/ts.400401","volume":"40","author":"HO Ilhan","year":"2023","unstructured":"Ilhan HO, Elbir A, Serbes G, Aydin N. The evaluation of nature-inspired optimization techniques for contrast enhancement in images: a novel software tool. Traitement Du Signal. 2023;40(4):1305\u201318.","journal-title":"Traitement Du Signal"},{"key":"407_CR30","doi-asserted-by":"crossref","unstructured":"Kaplan YA. Forecasting of global solar radiation: a statistical approach using simulated annealing algorithm. Eng Appl Artif Intell. 2024;136.","DOI":"10.1016\/j.engappai.2024.109034"},{"key":"407_CR31","doi-asserted-by":"crossref","unstructured":"Ilyas K, Younas I. Enhancing dynamic multi-objective optimization using opposition-based learning and simulated annealing. Int J Artif Intell Tools. 2023;32(04).","DOI":"10.1142\/S0218213023500379"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00407-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00407-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00407-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T14:28:16Z","timestamp":1757255296000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00407-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,18]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["407"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00407-5","relation":{},"ISSN":["2731-0809"],"issn-type":[{"type":"electronic","value":"2731-0809"}],"subject":[],"published":{"date-parts":[[2025,7,18]]},"assertion":[{"value":"17 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"158"}}