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The Holstein cow individual recognition network has the problems of high parameter adjustment cost, poor generalization and low efficiency, and it is difficult to achieve accurate recognition under partial occlusion conditions.An adaptive network parameter optimization identification algorithm (NAS-Res) was proposed based on ResNet framework and neural network architecture search (NAS). Firstly, a hyperparameter network was constructed by designing an operation set, including CBR_K1, CBR_K3, CBR_K5, and SkipConnect, together with dense connection paths. Then the search strategy based on gradient descent strengthened the design of a low-cost model under the constraint of multi-objective optimization composite loss function. The results showed that NAS-Res only took 6.18 GPU hours to obtain the best architecture.On the PO-Cows dataset, which contained side images of 168 cows, NAS-Res achieved 90.18% Top-1 Acc. Compared with ResNet-18, ResNet-34, and ResNet-50, the accuracy was improved by 5.04 percentage points, 3.02 percentage points, and 14.92 percentage points, respectively, while the parameters were reduced by 5.9×105, 1.069×107, and 1.317×107, respectively.It achieved 99.25% accuracy on the Cows2021 dataset, which contained 174 back images of cows. In addition, NAS-Res can ignore the influence of the scale change of the PO-Cows dataset, and when the number of cattle was changed between 50 and 168, the change range of Top-1 Acc and Top-5 Acc was only 1.51 percentage points and 1.01 percentage points, which showed strong applicability. In general, the NAS-Res algorithm achieved accurate individual identification of partially occluded cows, and the research result can provide technical reference for individual identification of livestock and poultry under complex background.