The existing group classification methods ignore the functional characteristics and their access probabilities implied in geographical positions. To solve this problem, a group classification method based on location semantic and probability is proposed, which includes two parts,location semantic discovery and the access probability vector clustering. Firstly, the location semantic implied in location words is obtained by using location semantic discovery method. Then according to the location semantic distribution, the access probability vector of mobile users for the location semantic space can be obtained. Finally, the group classification can be realized by using the access probability vector as the clustering weight vector. Experimental results show that the proposed method can effectively extract the location semantic coinciding with the reality and obtain similar users with similar access probabilities in location semantic space. Compared with the available group classification methods, the proposed method can achieve better experimental effects with an increase in F-measure of 4%.