The invention relates to an eigenvalue decomposition-fused low-complexity minimum variance ultrasound imaging method, and belongs to the field of ultrasound imaging. Firstly, echo data is converted toa beam domain with fewer dimensions by means of discrete cosine transform; and then a sample covariance matrix is subjected to eigenvalue decomposition to extract signal subspaces, a maximum eigenvalue and an eigenvector corresponding to the maximum eigenvalue are selected, remaining eigenvalues are the same value on the condition that it is guaranteed that a trace of the sample covariance matrixis invariable, and inversion of the matrix is simplified into multiplication of a vector. According to the algorithm, the operation time can be obviously shorter than that of an eigenvalue decomposition-based minimum variance algorithm, the good robustness is achieved on noise, and the imaging effect is obviously better than that of a traditional delay and sum algorithm, minimum variance algorithm and beam domain minimum variance algorithm.