Water is one of the necessary elements for our human survival, and the results of water quality monitoring are the basis for water quality control. In a region or watershed, many water quality monitoring points can be found in a region or watershed. With population growth, industrial development, and soil variety, water environment has undergone drastic changes, and some points may be wrongly, overly, or repetitively selected. As for this, resource-saving measures need to be taken to comprehensively show the distribution of water quality with as few points as possible. In this study, a method that combines auto-encoder neural network with hierarchical clustering is proposed. This method uses auto-encoder for feature selection of input samples and analyzes the samples after feature dimensionality reduction through hierarchical clustering, optimizing water quality monitoring points. The experiment results show that the method is more effective as opposed to the method of fuzzy clustering without feature dimensionality reduction.