As a new generation of the imaging device, light-field camera can simultaneously capture the spatial position and incident angle of light rays. However, the recorded light-field has a trade-off between spatial resolution and angular resolution. Especially the application range of light-field cameras is restricted by the limited spatial resolution of sub-aperture images. Therefore, a light-field super-resolution neural network that fuses multi-scale features to obtain super-resolved light-field is proposed in this paper. The deep-learning-based network framework contains three major modules: multi-scale feature extraction, global feature fusion, and up-sampling. Firstly, inherent structural features in the 4D light-field are learned through the multi-scale feature extraction module, and then the fusion module is exploited for feature fusion and enhancement. Finally, the up-sampling module is used to achieve light-field super-resolution. The experimental results on the synthetic light-field dataset and real-world light-field dataset showed that this method outperforms other state-of-the-art methods in both visual and numerical evaluations. In addition, the super-resolved light-field images were applied to depth estimation in this paper, the results illustrated that the disparity map was enhanced through the light-field spatial super-resolution.