The invention discloses a monocular light field image unsupervised depth estimation method based on a convolutional neural network. According to the method, the disclosed large-scale light field imagedata set is firstly used as a training set, and samples of the training set tend to be balanced through data enhancement and data expansion; an improved ResNet50 network model is constructed; an encoder and a decoder are used for extracting high-level and low-level features of a model respectively, results of the encoder and the decoder are fused through a dense difference structure, meanwhile, asuper-resolution shielding detection network is additionally constructed, and the shielding problem between all visual angles can be accurately predicted through deep learning; the objective functionbased on the light field image depth estimation task is a multi-loss function, the preprocessed image is trained through a pre-defined network model, and finally generalization evaluation is carriedout on the network model on a test set. According to the method, the preprocessing effect on the light field image of the complex scene is obvious, and the effect of more accurate light field image unsupervised depth estimation is achieved.