Aiming at the problems of large measurement delay and unpredictable production abnormalities that cannot be predicted in the manufacturing process of “ Chip / FPC on Glass” ( C/ FOG) for liquid crystal display ( LCD) panels, this paper proposes a virtual metrology method for C/ FOG manufacturing. This method uses sensors installed on the production machine to collect process state data during the production process and constructs a virtual metrology model based on multi-scale one-dimensional convolution and channel attention network (MS1DC-CA). Where the state data features in different scale ranges are extracted through multiple scale convolution kernels. In the preprocessing of original data containing missing values, an improved K-nearest neighbor interpolation method based on the particle swarm optimization algorithm (PSO-KNN Interpolation) is proposed to fill in the missing values. This method can reduce the interference introduced by filling values while retaining the features. Finally, it′s found that the actual defect rate is concentrated between 0. 1% and 0. 5% according to the experimental comparative analysis carried out for the data collected in actual production. The fitting mean square error of virtual metrology model is 0. 397 7 , which is lower than other existing fitting models. It also outperforms other existing fitting models under the evaluation indexes including mean absolute error, symmetric mean absolute percentage error and goodness of fit, which prouides the good predictive performance.