Target recognition based on 3D features has problems, such as easy misjudgment in similar point cloud domains and large amounts of total data computation, which results in a low target detection rate and high misjudgment rate. To improve the accuracy and speed of target recognition, a three-dimensional target recognition algorithm based on infrared features was proposed. The system simultaneously obtains the 2D infrared image and 3D point cloud data of the target area, obtains the projection range of the target using the salient features of the target's infrared characteristics, and calculates the pose relationship between the system and the target. The limited range of the target in the point cloud data is calculated according to the infrared feature mapping relationship, thereby significantly reducing the total number of point clouds that need to be matched and calculated. In the experiment, the same target vehicle was tested under the same background conditions, and the recognition data for three different test angles were recorded and analyzed. The obtained results indicated that the average target detection rate of the conventional point cloud recognition algorithm, average false positive rate, and convergence time were 93.4%, 19.5%, and 4.77 s, respectively. In addition, the average target detection rate of this algorithm, average false positive rate, and convergence time were 98.7%, 1.5%, and 1.23 s, respectively. It can be inferred that the detection and misjudgment rates of the target recognition algorithm based on infrared features are more advantageous, and the processing speed is faster.