College of Construction Engineering，Jilin University，Changchun 130026，China

In recent years，the bridge safety monitoring has been paid more attention in engineering field． However， the financial and material resources as well as human resources were costly for the traditional monitoring means． Besides，the traditional means of monitoring were low in accuracy． From an engineering example，based on neural network method and historical data of the bridge monitoring to construct the BP neural network model with dual hidden layer structure，the bridge temperature field and its effect on the behavior of bridge deflection are forecasted． The fact indicates that the predicted biggest error is 3. 06% of the bridge temperature field and the bridge deflection behavior under temperature field affected is 2. 17% by the method of the BP neural network， which fully meet the precision requirements of the construction with practical value．