Most traditional information hiding methods embed secret data by modifying cover data, which inevitably leaves traces of modification in cover data, and hence, it is difficult to resist the detection of the existing steganalysis algorithms. Consequently, the technique of coverless information hiding emerges, which hides secret data without modifying cover data. To improve the hiding capacity and robustness of coverless information hiding, this study proposes a constructive data hiding method based on texture synthesis and recognition with image style transfer. Firstly, natural images and texture images of different categories are used to construct the content image database and the textural style image database, respectively. A mapping dictionary of binary codes is established according to the categories of natural images in the content image database. Secondly, the labeled textural image database should be constructed and input into the convolutional neural network as a training dataset, and the texture image recognition model can be obtained by iterative training. In this way, the secret data can be extracted from stego images at the receiving end. During secret data hiding, natural images are selected from the content image database according to to-be-embedded secret data fragments, which are synthesized to form a stego mosaic image. Then, a texture image is randomly selected from the textural style image database, and the stego texture image can be generated by the selected texture image and the stego mosaic image with the strategy of style transfer to achieve secret data hiding. During secret data extraction, the obtained texture image recognition model can accurately identify the original categories of stego texture images corresponding to natural images, and secret data can be finally extracted by reference to the mapping dictionary. The experimental results demonstrate that the proposed method can achieve the stego texture image with a satisfactory visual effect and a high hiding capacity, and it illustrates strong robustness to attacks such as JPEG compression and Gaussian noise.