In recent years, image steganalysis algorithms have developed rapidly, and a series of advanced deep learning-based steganalysis models have been continuously proposed. However, existing steganalysis algorithms are unable to achieve precise weight allocation in areas with complex and smooth image textures, hindering their further development. To address the instability in feature extraction and analysis of steganalysis algorithms, this paper proposes a image steganalysis algorithm based on complexity matching and attention mechanism. First, using the characteristics of steganography algorithm tends to embed in texture complex region, designing complexity matching strategy, independently extracting texture complex block and texture smooth block features, aggregating strong steganographic signal region, and improving the extraction ability of the model for weak steganographic signals. Second, through the design of the improved convolutional attention mechanism, the attention to different image regions is effectively and reasonably allocated to improve the network for the important features and texture regions. The experimental results show that the proposed algorithm improves the steganalysis performance of several steganographic algorithms on two public datasets, BOSSBase v1.01 and Alaska2, compared with the existing models.