As the core component of automatic control system of hydraulic excavator, the reliability of hydraulic system pressure sensor directly affects the control performance of the whole excavator. To solve the key problem of loss of control system signal caused by pressure sensor failure under complex and severe working conditions, this study proposes a high-precision pressure data real-time prediction method based on depth learning. Firstly, based on the electro-hydraulic proportional control system of 37t hydraulic excavator, a test platform is established to collect the data of multi-source sensor under the actual excavation and loading operation condition; Secondly, the maximum information coefficient method is used to analyze the feature correlation, and the 125-dimensional original data is reduced to the 10-dimensional effective feature, and the high-quality data set is constructed by means of Kalman filtering and standardization; Then, the feature weight distribution module based on attention mechanism is designed, and the super-parameter configuration of long short term memory(LSTM) network is optimized by combining with sparrow search algorithm(SSA) to construct the SSA-LSTM-Attention fusion prediction model. Through the experimental verification of seven typical prediction models, such as convolutional neural network (CNN), gate recurrent unit (GRU) and LSTM, this method shows significant advantages in key pressure data prediction. The experimental results show that the mean absolute error and root mean square error of SSA-LSTM-Attention model are reduced by 54.45% and 54.56% respectively compared with the traditional LSTM model. The research proves that the proposed method can effectively solve the data compensation problem under sensor failure condition, and provide theoretical support for the fault tolerant design of intelligent control system of engineering machinery.