{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:03:26Z","timestamp":1775322206871,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program Project","award":["2019YFB1405303"],"award-info":[{"award-number":["2019YFB1405303"]}]},{"name":"National Key Research and Development Program Project","award":["BPHR202203233"],"award-info":[{"award-number":["BPHR202203233"]}]},{"name":"National Key Research and Development Program Project","award":["72174018"],"award-info":[{"award-number":["72174018"]}]},{"name":"The Project of Cultivation for Young Top-motch Talents of Beijing Municipal Institutions","award":["2019YFB1405303"],"award-info":[{"award-number":["2019YFB1405303"]}]},{"name":"The Project of Cultivation for Young Top-motch Talents of Beijing Municipal Institutions","award":["BPHR202203233"],"award-info":[{"award-number":["BPHR202203233"]}]},{"name":"The Project of Cultivation for Young Top-motch Talents of Beijing Municipal Institutions","award":["72174018"],"award-info":[{"award-number":["72174018"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFB1405303"],"award-info":[{"award-number":["2019YFB1405303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BPHR202203233"],"award-info":[{"award-number":["BPHR202203233"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72174018"],"award-info":[{"award-number":["72174018"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>To address the diverse needs of enterprise users and the cold-start issue of recommendation system, this paper proposes a quality-service demand classification method\u20141D-CNN-CrossEntorpyLoss, based on cross-entropy loss and one-dimensional convolutional neural network (1D-CNN) with the comprehensive enterprise quality portrait labels. The main idea of 1D-CNN-CrossEntorpyLoss is to use cross-entropy to minimize the loss of 1D-CNN model and enhance the performance of the enterprise quality-service demand classification. The transaction data of the enterprise quality-service platform are selected as the data source. Finally, the performance of 1D-CNN-CrossEntorpyLoss is compared with XGBoost, SVM, and logistic regression models. From the experimental results, it can be found that 1D-CNN-CrossEntorpyLoss has the best classification results with an accuracy of 72.44%. In addition, compared to the results without the enterprise-quality portrait, the enterprise-quality portrait improves the accuracy and recall of 1D-CNN-CrossEntorpyLoss model. It is also verified that the enterprise-quality portrait can further improve the classification ability of enterprise quality-service demand, and 1D-CNN-CrossEntorpyLoss is better than other classification methods, which can improve the precision service of the comprehensive quality service platform for MSMEs.<\/jats:p>","DOI":"10.3390\/e25081211","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:13:57Z","timestamp":1692008037000},"page":"1211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Enterprise Service Demand Classification Method Based on One-Dimensional Convolutional Neural Network with Cross-Entropy Loss and Enterprise Portrait"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4234-2060","authenticated-orcid":false,"given":"Haixia","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7841-9608","authenticated-orcid":false,"given":"Jindong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics & Management, Beijing Information Science & Technology University, Beijing 100192, China"},{"name":"Beijing International Science and Technology Cooperation Base of Intelligent Decision and Big Data Application, Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"ref_1","first-page":"12","article-title":"Empirical Analysis of MSMEs\u2019 Development demand for government services-based on a survey of 500 MSMEs in fuzhou city","volume":"265","author":"Chen","year":"2015","journal-title":"World Surv. 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