Under the environment of big data， with the rapid expansion of the online advertising industry， the online advertising calculation has attracted more and more attention. Computational advertising aims at placing ads on a specific audience， performs data analysis and calculation based on the advertising environment and user characteristics， and selects the best matching ad from the candidate ad library. The core issue is the calculation of click conversion rate prediction for online advertising， which selects the ads with the highest probability of users clicking. The accurate prediction of advertisement click conversion rate is related to benefits of publishers， advertisers and users. Based on the advertising data provided by the TrackMaster platform， this study analyzes user information features， advertising information features， context features and statistical features from the perspective of feature engineering. The larger effects on the advertising click conversion characteristics are excavated out. Layered advertisement click conversion rate prediction model is constructed and trained. The LightGBM algorithm model is adopted to obtain the important feature ranking of the ad click conversion rate. The experimental results indicate that when the feature selection threshold is 0.95， the number of feature choices is 19， and the number of trees is 100， the area under receiver operating characteristic （ROC） curve （AUC） value of the model is the maximum， and the logarithmic loss function value of the model is about 0.136 8. The model has the optimal effect. The prediction model and the result of feature ranking are helpful for the enterprise to make the optimal advertising strategy.