Nowadays, the Internet recommendation system has become a hot topic. Automatic recommendation has greatly facilitated people’s life and helped people find the most interesting key information from the massive information. Now news information is generated every moment on the Internet, and the existing information is a very large data set, which can help to count the user preferences and popularity of news content. At present, there are many kinds of recommendation systems on the Internet. They comprehensively consider the characteristics of users and articles to be recommended. Based on the data on various social media on the Internet, they build models and can use these models for accurate personalized user recommendation. The existing recommendation system is usually a supervised learning system which takes a lot of user characteristics into account. These methods often ignore the following issue: the recommendation strategy in the history is often imbalance. Through the existing historical records, we cannot guarantee an unbiased result. So in this study, we propose a kind of personalized recommendation based on counterfactual learning. This method has stronger theoretical guarantee and also shows better algorithm performance than existing methods in the experimental results.