Light field images contain rich spatial and angle information and are, therefore, widely used in three-dimensional reconstruction and virtual reality; however, the limited spatial resolution of light field pictures, notably the blurring of the image edge area, prevents their application and development due to the inherent constraints of light field cameras. A light field image super-resolution network based on feature interactive fusion and attention is proposed here because the spatial information in a light field subaperture image contains rich texture and high-frequency details and the angle information corresponds to the correlation between different views. Here, the feature extraction and feature interactive fusion modules completely fuse the spatial and angle information of the light field; the feature channel attention module refines high-frequency aspects of the images by adaptively learning effective information and suppressing redundant information; and the optical field structure consistency module preserves the parallax structure between optical field pictures. The performance of the proposed network is typically superior to that of the compared super-resolution network, according to the experimental results from five light field datasets.