Optimization of Personalized Service System for Student Work in Smart Campus Colleges and Universities with the Aid of Artificial Intelligence

In this paper, the LSTM model and DTW algorithm are combined to construct a recommendation model based on a combinatorial algorithm. In order to realize the model, a student employment work recommendation service system is designed using B/S architecture. And the system is applied to colleges and universities in order to analyze its optimization effect on personalized service of student work in colleges and universities. The recommendation model in this paper converges to a loss rate of about 0.12 when trained for 40 rounds. The algorithm in this paper achieves a precision rate, recall rate, and F1 score of 97.15%, 92.26%, and 91.77%, respectively, and maintains a precision rate of approximately 51% when the recommended employment units reach 50. The system in this paper can provide accurate employment unit recommendations for students with different characteristics. In addition, the overall average satisfaction of students for this paper’s system is high, with an average score of 4.314. In conclusion, the employment job recommendation service system constructed in this paper provides a scientific and effective solution for optimizing personalized student services.