In recent years, intelligent computing frameworks have been widely applied as implementation tools in artificial intelligence (AI) engineering, and the reliability of the frameworks is the key to AI implementation effectiveness. However, the reliability assurance of intelligent computing frameworks is challenging. On one hand, the code iteration of frameworks is fast, with difficult code testing. On the other hand, unlike traditional software, intelligent computing frameworks involve a large number of tensor calculations, and the code specification lacks the guidance of software engineering theory. To this end, existing research mostly employs fuzzy testing to localize defects. However, such a method can only accurately discover specific fault types, and it is difficult to guide developers and make them focus on software quality during the development process. Therefore, this study takes the popular intelligent computing frameworks (TensorFlow, Baidu PaddlePaddle, etc.) as the research object, selects multiple change features to build datasets, and conducts just-in-time prediction on the defects of the intelligent computing framework at the code submission level. Additionally, LDA is employed to mine codes and code submission information as new features, and then the random forest is adopted for prediction. Results show that the average AUC-ROC is 0.77, and semantic information can slightly improve the prediction performance. Finally, this study leverages an explainable machine learning method called SHAP to analyze the influence of each feature on the prediction output of the model. The findings are as follows. (1) The influence of basic features on the model conforms to traditional software development laws. (2) Code and semantic features in submitted information are important in the prediction result of the model. (3) The contribution of different features in different systems to the output of the prediction model varies a lot.