In the context of the significant data era, this paper explores the educational applications based on diagnostic learning analytics technology to improve personalized learning and teaching effects in the educational process. The study adopts a multidimensional feature fusion approach to construct a cognitive diagnostic model to predict learners’ knowledge status and future learning performance. Through actual data testing, the model can effectively predict the students’ knowledge mastery state and analyze the students’ learning process in depth. The experimental results show that the diagnostic model exhibits high efficiency and accuracy in predicting students’ knowledge mastery status, with an accuracy rate of 92.97%, significantly better than traditional teaching methods. In addition, the study explores the encoding method of learners’ multidimensional features and constructs a dynamic diagnostic model of test factors and student factors based on graph attention network. The study provides a new learning analysis and diagnostic method in the education field, which helps improve the effect of personalized learning.