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Then, advanced artificial intelligence techniques, including traditional machine learning and deep learning methods, were employed to perform sentiment analysis on music fragments. In the process of creating and validating the model, the combination of convolutional neural network and long term memory network shows excellent performance in various performance indicators. However, for some complex or culturally ambiguous music fragments, the model may also suffer from misclassification problems. This provides the direction for further optimization of future research aimed at achieving more accurate music emotion analysis.<\/jats:p>","DOI":"10.3233\/jcm-247488","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T11:52:43Z","timestamp":1723809163000},"page":"2611-2628","source":"Crossref","is-referenced-by-count":2,"title":["Using artificial intelligence to analyze and classify music emotion"],"prefix":"10.66113","volume":"24","author":[{"given":"Hongyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"issue":"2","key":"10.3233\/JCM-247488_ref1","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s00530-017-0542-0","article-title":"Affective content analysis of music emotion through EEG","volume":"24","author":"Hsu","year":"2018","journal-title":"Multimedia 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