Abstract: To reduce the computational complexity of the direction of arrival (DOA) estimation method and avoid the problem of observation direction selection in the combined information processing of pressure and particle velocity, a beamspace multi-signal classification (MUSIC) direction-finding method weighted by the principal eigenvector for the circular acoustic vector-sensor array is proposed. Under the optimization criterion of maximizing the ratio of total signal power to the noise power, the optimally weighted vector for a single vector sensor (i.e. the principal eigenvector) is solved. The beamforming matrix is designed by the principal eigenvector, and the estimation variance of MUSIC in the element space and beamspace of the acoustic vector circular array is compared theoretically. The relationships between the large eigenvalue and signal power of a single vector sensor, the eigenvector corresponding to the large eigenvalue, and its steering vector are derived. The effectiveness of the beamforming matrix based on the principal eigenvector is verified through simulation, and it is shown that the proposed method has lower computational complexity and better azimuth estimation performance. Furthermore, pool experimental results illustrate that the proposed method can accurately estimate the azimuth of the signal source.