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Traffic signs play an important role and significance in vehicle traffic. However, in intelligent traffic, the recognition rate of traffic signs is low and the recognition time is long due to the poor feature extraction effect. Therefore, a new rapid recognition method of traffic signs based on visual image and laser point cloud fusion is proposed. Two-sided filtering method was used to preprocess the original laser point cloud data. The location ranging values of laser point cloud in target space are obtained by normalized processing. The location of the target image is obtained by the ranging value, the traffic sign visual image is processed in a normalized way, the k-means clustering algorithm is introduced to process the image by diclustering, the ROI of the image is cut by the cutting template made, and the depth features of the traffic sign image are extracted. Combined with the secondary filtering features of convolutional neural network, the features after secondary filtering are re-calibrated, and finally the convolutional neural network model is used to realize the rapid recognition of traffic signs. The experimental comparison shows that the proposed method has a good feature extraction effect for all types of traffic signs, and the recognition rate reaches 89.74%, the recognition time is only 13.1s, and the highest recognition time is only 15.1s under interference, which verifies that this method can quickly and accurately identify all types of traffic signs.