Molecular clumps are the birth place of stars. A census of molecular clumps and comprehensive studying of their properties will help us to understand the star formation process and the evolution of the Galaxy and the Universe. As the MWISP (Milky Way Imaging Scroll Painting) project going to be completed, such a kind of studies become to be practicably feasible. With the large amount of data at hand, an algorithm that automatically identifies and verifies molecular clumps is urgently in need. For the widely used methods for the three-dimensional molecular line data, including GaussClumps, ClumpFind, FellWalker, and Reinhold, one has to input a number of parameters to control their performances, which need to be repeatedly optimized and visually inspected, and then to obtain a satisfactory result. Therefore it is a human-power and time consuming task to identify and verify clumps for large-scale survey data. To overcome the limitations of the traditional clump detection algorithms, artificial intelligence (AI) would be a good solution. Here we propose a 3D CNN (Convolutional Neural Network) method, which can perform the task automatically. The whole process is divided into two steps, i.e., identification and verification. First we use traditional method (ClumpFind) with low threshold to identify candidates. The verification is done by trained 3D CNN models. We have done a series of experiments using artificial data. The results suggest that our method is advantageous over the four traditional methods. Application of the method to the real MWISP data demonstrates that the performance of the 3D CNN method is also satisfactory.