Mobile app advertising is a kind of initiative advertising in the Internet advertising market. It can analyze users' interests and hobbies to target their interest advertisements, and then it improves their experience and brings huge benefits to advertising platforms and advertisers. Therefore, predicting the conversion rate of mobile app ads has become a significant research direction. Based on logistic regression and two gradient boosting tree models, this study proposes two integration models, SXL and BLLX, using the idea of stacking and averaging. It solves the problem that traditional prediction models have limited capabilities and cannot accurately predict the conversion rate. The experimental results on the dataset of Tencent 2017 social advertising competition show that the SXL and BLLX can effectively improve the prediction results of advertising conversion rate.