To improve the recognition accuracy and speed of point cloud targets, a target recognition system combining infrared image acquisition and laser scanning is designed, and a point cloud target extraction algorithm based on infrared boundary constraints is proposed in this paper. Firstly, edge enhancement and infrared features are used to grayscale the infrared image. Secondly, the target infrared image area is utilized to provide boundary constraints for point cloud target extraction, and the projection of 2D images to 3D point clouds is achieved by mapping scale functions, achieving coordinate system alignment. Finally, target recognition is realized using a set of point clouds which meets boundary constraints. The experiments are tested for vehicle targets in complex contexts and the results are compared between the traditional algorithm and the present algorithm. As the total amount of point clouds increases, the detection rate of traditional algorithm increases from 83.7% to 97.6%. The algorithm in this paper increases from 96.2% to 98.8%, and is less affected by the total amount of point clouds. This algorithm is effective in rejecting pseudo-targets, so its accuracy is more stable. The detection time of this algorithm is only 1/3 to 1/4 of the traditional algorithm, and this design has improved both target recognition accuracy and detection time, and has better practicality.