Large scale laser image recognition methods are limited by computational power and data processing speed, making it difficult to meet the needs of fast classification and recognition. The emergence of cloud computing provides ideas for solving this problem. Design a fast classification and recognition method for large-scale laser images under cloud computing. Using cloud computing platforms to perform mathematical morphology analysis and preprocessing on laser images, ensuring that the images maintain consistent morphology and features in subsequent processing. With the help of wavelet analysis technology on the platform, the preprocessed laser images are subjected to denoising processing, effectively removing noise interference in the images and improving image quality. After obtaining noise free images, the distributed resources of cloud computing platforms are further utilized to efficiently extract image features and obtain feature parameters that accurately describe image texture attributes. Combine these feature parameters with other features to construct a complete image feature vector. By comparing the feature vectors of different images, accurately determining their similarities and differences, achieving precise classification and recognition of laser images. Comparative analysis of experimental data shows that compared to traditional methods, this method has significantly improved processing speed and recognition accuracy. It can not only effectively process large-scale laser image data, but also significantly improve the accuracy and efficiency of image recognition, providing new ideas and methods for the development of laser image recognition technology.