Abstract: For the problem of concentration identification of nuclear material in nuclear weapon/material identification system, we used the Monte Carlo method, established a database of neutron signal obtained by fission of nuclear material driven by ²⁵²Cf-source under the condition of different distance and angle of detectors. Based on the convolutional neural network in deep learning area, a method for ²⁵²Cf-source-driven nuclear material concentration identification was constructed, thereby, the identification of test samples was realized. Then a contrast experiment was conducted with the BP neural network and K-nearest neighbor method. The experimental results show that using the constructed method, a high identification rate of 92.05% is got. The problem of the accuracy of the nuclear material concentration identification was affected by the change of the distance and angle of the detector is solved, and the accuracy of this method is better than that of the BP neural network and K-nearest neighbor methods. This paper provides a new idea for the ²⁵²Cf-source-driven nuclear material concentration identification.