围绕山区内河航道水位预测技术难题，分析了影响航道水位预测的环境因素和技术难点，总结了当前航道水位预测模型的技术方法，创新提出了一种新型的MHA-BiGRU（Multi-Head Attention-Bidirectional Gated Recurrent Unit）航道水位预测模型，将多头注意力机制引入到GRU（Gated Recurrent Unit）模型中，实现了模型对航道水位序列数据时间和空间等重要因素的特征权重划分，使模型聚焦影响航道水位变化的关键因素。模型以乌江下游航道为研究对象，通过建设水位和流速实时动态监测站，实现了监测数据集的建立，选取平均绝对误差（Mean Absolute Error，MAE）、纳什效率系数（Nash-Sutcliffe Efficiency coefficient，NSE）和均方根误差（Root Mean Square Error，RMSE）等参数作为评价指标，对该模型进行了验证。研究结果表明：模型通过多头注意力机制和双向循环神经网络的应用，提升了航道水位预测的性能，与传统的LSTM（Long Short-Term Memory）和GRU等经典时序预测模型相比，该模型具有更好的鲁棒性和更高的精度。最后将模型嵌入到系统平台中进行了示范应用，实现了航道水位的实时动态监测和中短期预测，具有较高的工程应用价值。

Focusing on the technical problems of inland waterway level prediction in mountainous area，the environmental factors and technical difficulties affecting waterway water level prediction are analyzed，the current technical methods of waterway water level prediction model are summarized, and a new waterway water level prediction model Multi-Head Attention-Bidirectional Gated Recurrent Unit(MHA-BiGRU is innovatively proposed．The multi-head attention mechanism is introduced into GRU model，and the characteristic weights of important factors such as time and space of waterway water level series data are divided by the model, so that the model focuses on the key factors affecting waterway water level change.．Taking the downstream of Wujiang River as the research object, the model realizes the establishment of monitoring data set by building a real-time dynamic monitoring station for water level and flow velocity. The parameters such as MAE, RMSE and NSE are selected as evaluation indexes to verify the proposed model. The research results show that the model improves the performance of waterway water level prediction through the application of multi-head attention mechanism and bidirectional cyclic neural network. Compared with traditional classical time series prediction models such as LSTM and GRU, the model has better robustness and higher accuracy. Finally，the model is embedded into the system platform for demonstration application, and the real-time dynamic monitoring and short-term prediction of waterway water level are realized，which has high engineering application value.