Direction of arrival （DOA） estimation is an important research area in array signal processing, where estimation accuracy is closely related to the array aperture. Increasing the array aperture can effectively improve DOA estimation performance. However, traditional methods usually rely on increasing the number of array elements to expand the aperture, which is limited by physical size and hardware costs in practical applications. Therefore, effective expansion of array aperture without increasing physical resources is worth studying. This paper proposes a multi-feature fusion based learning method for array aperture expansion. By employing a multi-scale convolution module to extract features from the received signals of a small-aperture array, and combining it with a channel attention module for adaptive weighted fusion of multiple features, the proposed method ultimately generates received signals of a larger-aperture array. Simulation results show that the proposed method can expand the array aperture based on single snapshot signal of small aperture array and significantly improves DOA estimation performance.