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Then, the two\u2010stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel\u2010level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis.<\/jats:p>","DOI":"10.1155\/2021\/9921095","type":"journal-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T22:51:50Z","timestamp":1619563910000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep Learning Image Feature Recognition Algorithm for Judgment on the Rationality of Landscape Planning and Design"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4078-3324","authenticated-orcid":false,"given":"Bin","family":"Hu","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,4,27]]},"reference":[{"key":"e_1_2_8_1_2","unstructured":"WangY. H. PhungA. N. MohaimenulI. LiY. C. andYangH. C. 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