TBM intercepting effect assessment is an important and complex job in the TBMD. In order to comprehensively consider the influence of various factors on the intercepting effect assessment in TBM intercepting combat, the assessment system is analyzed based on infrared imaging, ISAR imaging and maneuvering target tracking methods. In view of Neural Network' advantage in dealing with the complex problems, an assessment model with BP Neural Network is built, and the building process is discussed in detail. The standard BP algorithm with the problems that the convergence speed is slow and local minimum points are easily formed, is improved through adding momentum top and adjusting factors timely. Finally, the improved BP algorithm is simulated through living example and analyzed, the result shows that the improved algorithm is better in convergence speed and accuracy, and simultaneously verifies the validity and reliability of the model in TBM intercepting effect assessment.