Construction and Analysis of College Students’ Career Expectation Model Based on Data Mining

This paper aims to build and analyze a college student career expectation prediction model based on data mining and machine learning technology. The study used educational institution data and social media data to explore characteristics such as students’ academic performance, major choice, internship experience and online behavior to predict their career expectations and tendencies. The study used multiple models, including models A, B, C, and D, with model D based on a deep neural network to provide more complex and accurate predictions. In the methodology section, the processes of data collection, feature engineering, and model construction are introduced in detail, and the formulas and algorithm descriptions of each model are included. In the model construction and analysis section, the performance of each model is demonstrated and its accuracy and reliability are analyzed. The results analysis and discussion section includes a detailed analysis of the model results, as well as the application and effect of the model in actual cases. Finally, in the discussion and outlook section, policy suggestions and practical significance are put forward, and the application prospects and limitations of the model are discussed. This study provides a comprehensive approach to college student career expectation prediction and demonstrates the performance and applicability of different models. This is of great significance to educational institutions and policymakers, as it can help them better understand students’ career tendencies and provide students with better career planning and support.