Aiming at the monitoring and evaluation model of water quality resources, this study deeply discusses the application of image processing transfer learning based on TOPSIS algorithm. By combining the advantages of multi-attribute decision making analysis of TOPSIS algorithm with the feature extraction ability of transfer learning, we successfully constructed an efficient and accurate water quality evaluation model. During the study, we collected 1000 water quality image data covering various water quality indicators and environmental factors for training and testing the model. Utilizing transfer learning technology, we extracted profound features from extensive natural image databases and seamlessly transferred these features to the realm of water quality image analysis. By leveraging transfer learning, we were able to successfully apply sophisticated image processing algorithms to water quality evaluation, thereby significantly enhancing the model’s accuracy and generalization capabilities. In the model evaluation, we use a variety of evaluation indicators, including accuracy, recall and F1 scores. Experimental outcomes demonstrate the efficacy of the transfer learning model, which incorporates the TOPSIS algorithm, in water quality evaluation tasks. Impressively, it achieves an accuracy rate of 92%, surpassing traditional methods by a considerable margin of 20%. Furthermore, the model exhibits remarkable improvements in recall rate and F1 score, increasing by 18% and 19% respectively. Additionally, we conducted a thorough sensitivity analysis to gain deeper insights into the model’s performance. By adjusting the weights of different water quality indexes and environmental factors, we found that the model has high stability under different water quality conditions, and has strong ability to identify slight water quality changes.