With the continuous deepening of research on the security and privacy of deep learning models, researchers find that model stealing attacks pose a tremendous threat to neural networks. A typical data-dependent model stealing attack can use a certain percentage of real data to query the target model and train an alternative model locally to steal the target model. Since 2020, a novel data-free model stealing attack method has been proposed, which can steal and attack deep neural networks simply by using fake query examples generated by generative models. Since it does not rely on real data, the data-free model stealing attack can cause more serious damage. However, the diversity and effectiveness of the query examples constructed by the current data-free model stealing attack methods are insufficient, and there are problems of a large number of queries and a relatively low success rate of the attack during the model stealing process. Therefore, this study proposes a vision feature decoupling-based model stealing attack (VFDA), which decouples and generates the visual features of the query examples generated during the data-free model stealing process by using a multi-decoder structure, thus improving the diversity of query examples and the effectiveness of model stealing. Specifically, VFDA uses three decoders to respectively generate the texture information, region encoding, and smoothing information of query examples to complete the decoupling of visual features of query examples. Secondly, to make the generated query examples more consistent with the visual features of real examples, the sparsity of the texture information is limited and the generated smoothing information is filtered. VFDA exploits the property that the representational tendency of neural networks depends on the image texture features, and can generate query examples with inter-class diversity, thus effectively improving the similarity of model stealing and the success rate of the attack. In addition, VFDA adds intra-class diversity loss to the smoothed information of query samples generated through decoupling to make the query samples more consistent with real sample distribution. By comparing with multiple model stealing attack methods, the VFDA method proposed in this study has better performance in the similarity of model stealing and the success rate of the attack. In particular, on the GTSRB and Tiny-ImageNet datasets with high resolution, the attack success rate is respectively improved by 3.86% and 4.15% on average compared with the currently better EBFA method.