There are many fields involved in human research on animal vocal behavior, such as biomedical science, ecology, and behavioral science. Understanding animal language and behavior remains a major focus of scientific research, and many efforts are being made in this direction. With the development of artificial intelligence technology, deep learning has gradually become a major method for humans to understand animal behavior sound. This article aims to develop an end-to-end animal behavior sound classification model using Wav2Vec2.0 (W2VCM). By combining the Wav2Vec2.0 model with the CatMeows dataset, a pre-trained model is obtained for extracting speech features and performing animal behavior sound classification tasks. Experimental results demonstrate that W2VCM achieves higher accuracy than six other deep learning models tested, both on the original and re-divided datasets. The accuracy of W2VCM reached 97.97%, 2.03% higher than the best result of other models on the original dataset, and 3.13% higher on the re-divided dataset.