Artificial intelligence (AI) has been widely used in AIEd (AI empowers education industry). In this paper, we conduct mining and prediction of influencing factors of student performance based on education data set. First, we apply data mining and analysis methods to two public educational data sets. Next, we further analyze the results through visual analysis method and try to explain the physical meaning. Then, we screened the feature data through two computing methods based on random forest algorithm. Finally, we predicted the student performance through an improved model based on K-means and Deep Neural Network (DNN). Our results show that the proposed model feature selection Adaptive-K-means-DNN obtains the best mean of squared residuals both in two student learning data sets; the results demonstrating that the feature selection processing and the employment of the adaptive K-means have enhanced the prediction performances significantly. Furthermore, we also find that mother’s education is the direct influence of the students’ final grade, the difference is there is no strong correlation between absences and the students’ final grade, but absences is still an important factor affecting the students’ final grade, and it may affect the students’ final grade through other factors; moreover, the failures also has a strong correlation between the students’ final grade, and it also is a direct influence indicator for the students’ final grade; in summary, the influence of mother’s education on students’ performance is very important. The management of students’ classroom absence will effectively improve students’ final performance. In addition, regular encouragement may be a good way for students to improve their learning performance.