Improving the velocity spectrum resolution is challenging because the field data contains some noise and polarity inversion in many cases. Although the AB semblance and local similarity method can deal with the seismic data with single polarity reversal, they do not perform as well for noisy and multi-AVO data. In this paper, we proposed a symbolic-weighted velocity analysis velocity spectrum analysis method to solve these problems. Firstly, to correct the polarity of multiple AVO seismic data, we adopt a technology using the symbolic information between adjacent traces. Then, considering the singular value distribution characteristics of seismic data in velocity scanning, the RSVD algorithm which is a modified mode of the singular value decomposition is used to improve the recognition sensitivity of hyperbolic events and suppress the influence of noise. Synthetic and field examples demonstrate that, compared with conventional semblance, local semblance and SBWS, the proposed method has higher precision and stronger anti-noise performance for the noisy multi-AVO seismic data.