College Student Employment Recommendation Algorithm Based on Convolutional Neural Network

The employment of graduates is one of the important evaluation indicators of the quality of running a school. It monitors the employment of graduates in real time, improves employment guidance, improves the quality of employment, and delivers qualified talents to the society. Accelerating school development plays a vital role. The purpose of this paper is to study the research and design of college students' employment recommendation algorithm based on convolutional neural network. The reasons for studying employment recommendation algorithms and their functions and advantages are introduced. Then, the research progress of employment recommendation is expounded, and the current problems and challenges faced by the employment recommendation algorithm are introduced. The processed text data is vectorized using the knowledge distillation-based job corpus model proposed in this paper. Then use the fusion algorithm based on CNN and BILSTM to locate the occupation category on the vectorized data. Finally, the cosine similarity algorithm is used to recommend personalized jobs for college talents. The experimental results show that the algorithm has a recall rate of 97%, which can effectively solve the problems of current college graduates' employment confusion and difficult positioning.