Abstract: In order to accurately predict the dissolved oxygen content in water quality parameters, we adopted a Long Short Term Memory (LSTM) model, and proposed an Enhanced Sparrow Search Algorithm (ESSA) to improve the accuracy of the prediction rate. Besides, to prompt individual sparrows to swiftly depart from the local optimal solution, the algorithm introduced Circle chaotic mapping for population initialization, and integrated sine-cosine algorithm and Levy flight strategy to update the positions of scouts and trackers, respectively. Firstly, we compared ESSA with various other algorithms for multi form benchmark function testing, and the results reveal that the algorithm exhibited excellent performance and robustness on multiple benchmark functions. Subsequently, we used ESSA to explore LSTM model parameters and compared it with other optimization strategies, and the results show that the prediction rate based on ESSA-LSTM model reached 99.071%, which was improved by 2.142%, 6.653%, 6.682% and 7.714% compared with basic Sparrow Search Algorithm (SSA), Gray Wolf Optimization Algorithm (GWO), Marine Predation Algorithm (MPA), and Whale Optimization Algorithm (WOA), respectively. The results show that the use of ESSA significantly improves the prediction rate of dissolved oxygen (DO) and effectively reduces the blindness and time cost of parameter settings.