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The study first collected students\u2019 questionnaires and academic records, and used the k-means clustering algorithm to cluster students\u2019 personal characteristics to identify key factors affecting career development, such as major choice, academic performance, and academic activities. In order to deeply understand the dynamic changes of students\u2019 career tendencies, the study introduced the LSTM model, which was analyzed based on the students\u2019 four-year long-term time series data to predict their career development trends. Experimental data show that this method has improved prediction accuracy and correlation compared with traditional multiple linear regression and convolutional neural networks, with prediction accuracy and correlation reaching 0.847 and 0.945, respectively. 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