For the problems of low degree of freedom and low accuracy in near-field source localization， a localization algorithm based on fourth-order cumulant matrix is proposed. Firstly， a high-dimensional virtual covariance matrix is constructed， where the equivalent steering vector contains both direction of arrival （DOA） and distance information. In angle estimation， a one-dimensional search method based on rank deficiency to search the reciprocal of the minimum singular value is proposed， where the computational burden is reduced. The degrees of freedom are increased and the characteristic that the high-order cumulant of Gaussian noise is zero is exploited to improve the estimation performance at low signal-to-noise ratio. In the estimation of distance， the distance information contained in the singular vector obtained by singular value decomposition in angle estimation can be directly exploited without additional calculation， and the distance is estimated by the least square method. Simulation results show that the method estimates the angle and distance information of the near-field source through the one-dimensional search only in a high-order cumulant matrix， which reduces the computational burden and improves the accuracy of the estimation compared with the existing algorithms. Moreover， the proposed method has twice as many degrees of freedom as the reduced-dimension MUSIC method.