In order to improve the detection ability of the terminal sensitive projectile to the armored target under complex background conditions, the linear array LiDAR is used as the detector and the convolutional neural network is combined to classify and identify the range profile of the linear array LiDAR. The range imaging of the scanning area is realized by using the steady-state motion characteristics of the terminal sensitive projectile dropping while rotating. The original range profile is constructed into a range profile suitable for convolutional neural network by sampling rate control and interpolation. The convolutional neural network consisting of two convolutional layers and one full link layer are established to meet the requirements of high real-time, small size and low power consumption on missile-borne. Xilinx ZYNQSoC chip is selected as hardware platform, and hardware acceleration is achieved by placing convolution operation on PL end of ZYNQSoC based on HLS technology and SDSoC development environment. The scaled simulation experiment proves that the method has high target recognition accuracy and can effectively recognize armored targets in complex background. Through placing convolution operation on PL end of ZYNQSoC, the performance acceleration is five times faster than that of CPU, which can meet the requirements of missile-borne.