Zero-watermarking technology is an effective means of protecting image copyright. However, most of the existing zero-watermarking algorithms use traditional mathematical theories to extract features manually, and extensive research on zero-watermarking extracting image features with neural networks is still to be conducted. At present, neural networks have achieved favorable results in image feature extraction. A deep attention mechanism and autoencoder (AMAE) model is proposed for constructing zero-watermarks by making full use of a convolutional autoencoder and the attention mechanism. Specifically, an attention-based convolutional neural network is used to construct an autoencoder, which is then trained. Subsequently, the global features of the image are constructed with the features output from the trained encoder. Finally, binary pattern processing of the obtained feature image is conducted to acquire the binary feature matrix. An XOR operation with the image to be watermarked is then performed to obtain a zero-watermark, which is then registered into the intellectual property database. Once the zero-watermark is registered, the original image is under the protection of watermarking technology. During training, the idea of adversarial training is drawn on to train the model with noise, which improves the robustness of the model. The experimental results show that the normalized correlation (NC) values of the extracted watermarked image and the original one to be watermarked both exceed 0.9 under rotation, noise, filtering, and other attacks, which proves the effectiveness and superiority of the proposed algorithm.