The existing light field image saliency detection algorithms cannot effectively measure the focus information, resulting in an incomplete salient object, information redundancy, and blurred edges. Considering that different slices of the focal stack and the all-focus image play different roles in saliency prediction, this study combines the efficient channel attention (ECA) network and convolutional long short-term memory model (ConvLSTM) network to form a feature fusion network that adaptively fuse the features of the focal stack slices and all-focus images without reducing the dimension; then the feedback network composed of the cross feature module refines the information and eliminates the redundant information generated after the feature fusion; finally, the ECA network is used for weighing the high-level features to better highlight the saliency area to obtain a more accurate saliency map. The network proposed has F-measure and mean absolute error (MAE) of 0.871 and 0.049, respectively, in the most recent data set, which are significantly better than the existing red, green, and blue (RGB) images, red, green, blue, and depth (RGB-D) images, and light field images saliency detection algorithms. The experimental results show that the proposed network can effectively separate the foreground and background regions of the focal stack slices and produce a more accurate saliency map.