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With the rapid development of intelligent animal husbandry, bovine face recognition has become the key to intelligent cattle breeding, but the problem of bovine face occlusion in practical application scenarios is more serious, which brings challenges to the performance of the recognition system. To solve this problem, a two-branch network structure based on occlude-assisted bovine face recognition was proposed. Firstly, an improved lightweight U-Net occlusion segmentation model was designed. By adding deep separable convolution and multi-scale mixing pool module, the occlusion segmentation performance of the segmentation network was effectively improved. Secondly, in order to better attenuate the influence of occlusions on bovine face recognition performance, a multilevel mask generation unit was introduced, and masks corresponding to different stages of the recognition network were constructed with different levels of occlusions as input. The damaged feature information caused by occlusions was effectively eliminated in each stage of feature extraction through mask operation. Finally, for the validity and real-time performance of the detection algorithm, the algorithm was verified on the selfmade data set, and compared with a variety of recent typical recognition algorithms. The experimental results showed that the proposed algorithm had an average accuracy of 86.34% on the blocked cow face data set, and the recognition speed was 54 f/s. Compared with the single-scale mask, the average accuracy of multistage mask was improved by 2.02 percentage points, and the recognition effect was better than that of the comparison network under different degrees of occlusion.