The invention provides a position recommendation method, comprising the following steps S1: acquiring user access data and generating a data matrix; 2, executing a supervised learning algorithm on that data matrix; 3, executing an unsupervised learning algorithm on that data matrix and generating prediction result data; 4, generating position pre-recommendation data according to that predict result data and the character portrait data. The invention has the beneficial effects that the system automatically learns, completes user analysis, and accurately recommends work information according tothe visitor's equipment information, access content, operation behavior, etc. through the machine learning method of post pre-recommendation. It enables users to find more highly relevant and interesting position information, and improves user viewing content, so as to have a higher user conversion rate.