Abstract: In order to better meet the needs of the bridge health monitoring system and improve the performance of the bridge health monitoring system, this paper combines deep learning with the time series database InfluxDB to build a new early warning mechanism for the bridge health monitoring system and improve the risk perception ability of the modern bridge health monitoring system. This paper took the Ganjiang River Bridge as the background and combined the neural network CNN with the long short-term memory network to construct a CNN-LSTM model to predict the deflection data of the bridge. Through the analysis of the experimental results, it is found that the CNN-LSTM model can effectively predict the deflection data of the bridge. When the confidence interval is ±0.1 mm, the accuracy rate reaches 92.8%. In predicting the deflection data of the next ten minutes, the root mean square error (RMSE) is 0.109 7. Practice shows that the fusion of the time series database InfluxDB and the CNN-LSTM model enhances the bridge health monitoring system's perception of potential threats, and effectively improves the early warning and alarm mechanism of the bridge health monitoring system.