Bridge construction monitoring often has problems such as strain and displacement sensor damage failure. Therefore, this paper takes Nanliu Jialing River Bridge as the background, based on the K-Nearest Neighbor (KNN) algorithm to supplement the missing data of construction monitoring, and uses ANSYS Workbench to establish the FEM of bridge construction process, which proves the effectiveness of KNN algorithm in the absence of bridge construction monitoring data. The results show that the KNN algorithm can help solve the problem of blank monitoring data in a short time after the damage of strain and displacement sensors during bridge construction. The stress of the root section of the box girder increases with the increase of the cantilever length, and the measured stress value of the bottom plate is gradually close to the theoretical value. The stress value of the root section of No.8 pier is too large, which may be caused by the initial reading error of the strain gauge. The alignment of the main girder of the cantilever construction of No.8 pier is in good agreement with the expectation and gradually tends to the target line. The elevation of the left flange plate of the main girder section of the Qingniu side of the cantilever construction of No.10 pier is less than the target elevation. At the same time, the elevation of the roof center of the main girder section of the Tiger jump side is less than the target elevation, which needs to be adjusted in time in the subsequent construction process.