In this paper, we propose a hybrid convolutional neural network architecture that can identify the melt pool depth (MPD), a melt pool parameter that is strenuous to extract with conventional sensing techniques, as a continuous value based on 2-dimension melt pool images in powder bed fusion (PBF) additive manufacturing. The melt pool images and their corresponding geometric parameters were collected from finite element methods. The average coefficient of correlation between the model prediction value and the true label value by five-fold cross validation is 0.980, demonstrating the effectiveness of the proposed architecture.