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The large difference between the recorded water level data and the original water level data is the main reason why the station host cannot accurately predict the characteristics of the surrounding water system. To solve the above problem, a bidirectional LSTM neural network based station surrounding water level prediction system is designed. The hardware part of the water level prediction system around the station is designed with a peripheral water system query system and water level recording device; On this basis, based on the initial parameter definition results, an LSTM neural network layout model is established, and the connection loop of the bidirectional LSTM decoder in the water level prediction system is improved to achieve the overall execution plan design of the water level prediction system around the station. Collect water level data and implement targeted cleaning treatment. Using the cleaned data object, establish a one-dimensional hydrodynamic model. Then, based on the calculation results of water system roughness, determine the numerical relationship between flow rate and delay time, and analyze the characteristics of the station and its surrounding water system. Combined with relevant software and hardware structures, complete the design of a bidirectional LSTM neural network based station surrounding water level prediction system. The experimental results indicate that the application of the above system can ensure error free fitting between the water level recorded data and the original water level data, and will not lead to inaccurate prediction of water system characteristics due to the large difference in water level data.