In this study, the application of TOPSIS algorithm based on neural network in water quality resource image processing is deeply explored, and its practical application effect is evaluated through detailed data analysis. In this study, a large number of water quality resource images are used as samples, feature extraction and classification are carried out by neural network, and then TOPSIS algorithm is used for water quality assessment. A comprehensive series of experiments, coupled with rigorous data analysis, has validated the effectiveness of the TOPSIS algorithm, when integrated with neural network technology, in the realm of water quality resource image processing. The experimental findings unequivocally showcase the exceptional proficiency of this integrated methodology in the realm of processing water resource images, thereby highlighting its immense potential and broad applicability within this domain. Encompassing a comprehensive dataset of 1000 water quality image samples, which encompassed a diverse array of scenarios such as clear water, turbid water, water samples contaminated with various pollutants, and other types, the study underscores a significant leap in performance. When juxtaposed against conventional methodologies, the neural network, seamlessly fused with the TOPSIS algorithm, displays a stunning enhancement in image processing accuracy. This advancement translates into a noteworthy 15% increase, propelling the accuracy level from a solid 80% to an impressive 95%. This achievement not only validates the superiority of the proposed approach but also underscores its potential to revolutionize water quality monitoring and assessment through advanced image analysis. Simultaneously, the rate of misjudgment has been significantly reduced by 10%, falling from 15% to just 5%. Notably, in the recognition of clear water samples, the algorithm achieves an astonishing accuracy rate of 98%, while turbid water samples are recognized with a 95% accuracy. Even for water samples containing various pollutants, the average accuracy rate remains impressive at 92%. Furthermore, this algorithm demonstrates superior processing speed, enhancing it by nearly 30% compared to traditional methods.