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Compared with traditional neural networks, deep learning networks have deeper and more network layers and can learn more complex processing functions. In this stage, image recognition technology can be used as an assistant of human vision. People can quickly get the information in the picture through retrieval. For example, you can take a picture of an object that is difficult to describe and quickly search the content related to it. Convolutional neural network, which is widely used in the image classification task of computer vision, can automatically complete feature learning on the data without manual feature extraction. Compared with the traditional test, the test can reflect the painting characteristics of different groups. After quantitative scoring, it has good reliability and validity. It has high application value in psychological evaluation, especially in the diagnosis of mental diseases. This paper focuses on the subjectivity of HTP evaluation. Convolutional neural network is a mature technology in deep learning. The traditional HTP assessment process relies on the experience of researchers to extract painting features and classification.<\/jats:p>","DOI":"10.1155\/2021\/6370509","type":"journal-article","created":{"date-parts":[[2021,10,11]],"date-time":"2021-10-11T04:07:37Z","timestamp":1633925257000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Image Classification in HTP Test Based on Convolutional Neural Network Model"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4988-8026","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"first","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.image.2016.06.007"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2018.2859203"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/9240407"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.14311\/nnw.2020.30.011"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2018.02617"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01703-3"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/lgrs.2018.2880136"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2019.2909695"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"FukuiH. 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