Aiming at the shortage of real-time performance of the hierarchical convolutional features for visual tracking algorithm and the poor adaptability of single classifier to target appearance changes, a real-time visual tracking algorithm based on multiple Gaussian-distribution correlation filters was proposed. Features with high dimensions of convolution channels were extracted from Pool4 and Conv5-3 layers of VGG-19 networks, and the sparse sampling approach was used to reduce the number of convolution channels to speed up the tracking algorithm. In order to prevent the decrease of tracking accuracy caused by the reduction of features, the multiple correlation filters based on different Gaussian-distribution samples were trained and all the predicted target positions were fused by adaptive weights, expecting for the better robustness for target posture changes. The sparse model update strategy was applied to further improve the algorithm’s speed and achieve the real-time performance. Experimental results on OTB100 benchmark dataset showed that the proposed algorithm had an average distance precision of 86.6%, which was 3.5% higher than that of the original hierarchical convolutional features for visual tracking method. The proposed method has better robustness under complex conditions, for example occlusion, deformation, similar background interferences. The average tracking speed was 43.7 frames per second, and it had a better real-time effect.