In order to improve the accuracy and efficiency of collaborative filtering recommendation method, a collaborative filtering recommendation method based on improved heuristic similarity model, namely PSJ, was proposed, which considered the difference of user ratings, the user global rating preferences and the number of common rating items. The Proximity factor of PSJ method used the exponential function to reflect the influence of the difference of user ratings, which avoided the problem of zero divider. The Significance factor of NHSM (New Heuristic Similarity Model) method and the URP (User Rating Preference) factor were merged to build the Significance factor of PSJ method, which makes the computational complexity of the PSJ method be lower than that of NHSM. To improve the recommendation performance in data sparsity conditions, both the variance value of user ratings and user global rating preferences were considered in PSJ method. In experiments, precision and recall of Top- k recommendation were used to evaluate the results. The results show that compard with NHSM, Jaccard algorithm, Adjust COSine similarity (ACOS) algorithm, Jaccard Mean Squared Difference (JMSD) algorithm and Sigmoid function based Pearson Correlation Coefficient method (SPCC), the precision and recall of PSJ method are improved.