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Abstract Aiming at the problem that the linear regression of the traditional linear water quality prediction model is not robust enough and the prediction accuracy cannot be guaranteed in the presence of interference, this paper proposes an agricultural water quality prediction model based on the Momentum algorithm to optimize the logistic regression algorithm (LRM algorithm). The model uses the Momentum algorithm to optimize the logistic regression algorithm to quickly adjust the misclassified samples. When the object encounters a local optimum in the process of falling, the introduction of momentum makes it easy for the next update to jump out of the local optimum with the help of the last large gradient. In this paper, the performance of the proposed model is evaluated on 4 real data sets. The experimental results show that the LRM algorithm proposed in this paper improves the prediction accuracy of the existing algorithm by an average of 1.11 percentage points.Compared with KNN and other traditional prediction algorithms, LRM not only speeds up the convergence rate of the algorithm, but also reduces the steady-state error and improves the prediction accuracy of water quality, suitable for data mining of complex water quality data, The experiment verifies the feasibility of this method in predicting the actual agricultural water quality and even in predicting and warning the residents drinking water.