At present，the prediction of the hydraulic fracturing effect of oil production wells in Daqing Oilfield mostly relies on experience or simple models such as multiple linear regression，which leads to poor stability of prediction results and low prediction accuracy. With Block N23 of Daqing Oilfield as an example，the correlation between the fracturing effect of oil production wells and influencing factors is analyzed by the mathematical statistics. The influence of those factors on the hydraulic fracturing effect in Block N23 is studied by a random forest algorithm. Additionally，the principles and implementation methods of meta learning，Bayesian optimization，and model ensemble in automatic machine learning are presented，and a prediction model of a data-driven hydraulic fracturing effect based on the automatic machine learning technology is constructed. Meanwhile，the model is compared with three common machine learning algorithms：random forest，support vector machine and neural network. The proposed model is employed to design and optimize the hydraulic fracturing of Block N23. The results show that the production parameters before fracturing exert an important influence on predicting the effect of oil production well after fracturing. The model constructed by the automatic machine learning algorithm has higher accuracy than other algorithms. The determination coefficient on the test set is 0.695，and the average relative prediction error is 18.96%，which is 57.53% lower than the current level. Compared with the original one，the fracturing scheme optimized by the model can increase the economic benefit by about 3.2×104-27.4×104 yuan per well.