Prophet is a time series prediction model open sourced by Facebook. It is good at processing daily periodic data with large outliers and trend changes. Aiming at the problem that Prophet time series model has low prediction accuracy on short-term data, this paper proposes an improved Prophet_SVR model based on Prophet to predict dissolved oxygen parameters in the next 2 hours, and uses a comparative model to perform comparative experiments on the same data. The experimental results were compared by root mean square error (ERMSE) and average absolute percentage error (EMAPE). The results show that the compared with Prophet time series model, in Prophet_SVR model, ERMSE is reduced by 0.197 1 and EMAPE is decreased by 3.890 4%. Through experimental comparison, it can be seen that the Prophet_SVR prediction model is more effective in reducing the overall prediction error and improving the accuracy of a single numerical prediction, and the method can train the model in a shorter time and with higher efficiency, which provides a reference for short-term water quality parameter prediction.