Assembly correctness identification of internal part of complex component is one of the essential processes for industrial product testing. However, there is still lack of a detection method with high systematic robustness to improve the whole testing process. To solve this problem, based on the convolution neural network classification and computed tomography (CT) technology, we propose a detection method to identify automatically the area of interested image, which is different from the detection methods characterized by the connected area in the past. Thus, the judgment criteria of the qualified products is changed from the regional characteristics to individual characteristics. The sequence of projection data collected by CT system is input to the convolutional neural network model to precisely locate and classify the internal parts of the workpiece. The result of the internal components classification is taken as the criterion of the detection for the missing parts. The projection of standard workpiece is matched to the projection of the test-workpiece, which can detect the displacement of the parts. The experimental results show that the method can identify missing and misaligned internal parts of the workpiece in the simulation and the experiment. The overall system is robust for the situation such as overlapping among the internal parts of the workpiece.