Research on the Construction of Prediction Model of College Students' Employment under Deep Learning Technology

Facing the severe employment situation of college students, the analysis and prediction of employment situation is increasingly important in the employment guidance system of colleges and universities, which can not only help students make more wise career choices, but also help colleges and universities adjust their employment guidance strategies. However, the current employment forecasting methods in colleges and universities have some limitations in forecasting accuracy and execution efficiency, and it is difficult to meet the practical application needs of colleges and universities. In view of this problem, this paper will deeply explore the application feasibility of artificial intelligence and deep learning technology in this field, and put forward a brand-new prediction model for college students' employment, so as to improve the feasibility and effectiveness of the employment prediction method. Practice has proved that the model combines the advantages of Convolutional Neural Network (CNN) and Long-term and Short-term Memory Neural Network (LSTM) in feature extraction, time series processing and multi-classification prediction, and automatically learns the complex nonlinear relationship among various factors affecting college students' employment, thus overcoming the shortcomings of traditional prediction methods and providing an efficient, accurate and forward-looking employment prediction tool for colleges and universities.