As a convenient chemical sensor, pH electrode is widely used in the measurement of pH value of water bodies. However, due to structural aging and environmental influences, pH electrode is prone to drift, which directly results in the inability to obtain accurate measurement results. Based on the above problems, this paper proposes a cascade structure soft compensation model with the Gated Recurrent Unit (GRU) as the main body. The model uses the complete ensemble empirical mode decomposition with adaptive noise with permutation entropy (CEEMDAN-PE) method to obtain the main characteristics of the pH electrode potential drift signal to reduce the interference of noise in the actual measurement environment, and uses its output as the input of the GRU neural network to obtain the prediction result and compensate. This model is called the CEEMDA-PE & GRU (CPG) model. In this paper, the CPG model is compared with the commonly used time series prediction model, and the results show that the prediction effect of this model is better than other models. Root-mean-squared-error (RMSE), mean-absolute-error (MAE), and mean-absolute-percentage- error (MAPE) of the prediction model are reduced by 60.97%, 65.53%, and 66.55% respectively. Finally, this paper proposes the concept of compensation degree to evaluate the compensation effect. The average compensation degree of the soft compensation method is above 83%. It shows that the soft compensation method can improve the measurement accuracy of pH electrode and has good robustness.