In order to realize the intelligent prediction of valve inlet water temperature of a voltage sourced converter-high voltage direct current (VSC-HVDC) valve cooling system,a prediction model of inlet water temperature of VSC-HVDC based on a hybrid model of the random forest (RF) and bi-directional long short-term memory (BiLSTM) is proposed,and the cooling capacity of the cooling system is evaluated on the basis of the proposed prediction model. Firstly,a RF algorithm is used to analyze the importance of high-dimensional feature sets,which consist of all the monitoring variables of the valve cooling system. Then the important characteristic parameters affecting the inlet water temperature are filtered out to form an input feature vector with the historical inlet water temperature. Secondly,the feature vector is input to the developed BiLSTM prediction model to train the model for accurately predicting inlet valve water temperature and quantitatively evaluating the cooling capacity. Finally,a VSC-HVDC converter station in Guangdong power grid is taken as an example to verify the effectiveness and superiority of the proposed method. Simulation results indicate that the accuracy of the proposed hybrid model based on RF-BiLSTM is higher than that based on BiLSTM model,RF model,support vector machine (SVM) model and auto-regressive and moving average (ARMA) model. Moreover,the cooling capacity is evaluated quantitatively and accurately. Analysis results show that the cooling margin of this converter station is up to 98%,which indicates that there is a problem of overcooling and energy waste. The evaluation result of the cooling capacity is well consistent with the field operation result of the converter station,which confirms the effectiveness and the accuracy of the proposed method.