Research on the Application and Effect Analysis of Deep-network Learning-based Image Recognition Optimization Algorithm in the Field of Advertisement

In the field of apparel advertising, the accuracy of image style recognition is crucial for accurate marketing and user experience. Traditional manual feature extraction methods are inefficient and have limited accuracy. In this paper, we innovatively propose a recognition method based on improved Res-Net 152 network with migration learning. The method enhances the multi-scale feature learning capability by optimizing the network structure, and accelerates the convergence and improves the performance by using Image-Net pre-trained model. After fine-tuning the data for the girl’s clothing advertisement, the experiment shows that the recognition accuracy reaches 94.2%, which is much better than the traditional method and significantly improved than other methods (29.2%, 8.3%, 1.3%). This method not only improves the recognition accuracy, but also optimizes the training process, helping the intelligent transformation of the advertising industry, and is expected to be expanded to more advertising image recognition tasks in the future.