As spectral clustering is sensitive to the scaling parameter while calculating the affinity matrix and the result of clustering multi-scale dataset is not ideal, an improved adaptive spectral clustering algorithm based on density adjustment is proposed. The algorithm introduces local density of data into spectral clustering, using the density difference to adjust the similarity between sample points, which makes it more consistent with the data points’ internal relations of the clusters’ actual structure. So that it solves the multi-scale clustering problem to some extent. At the same time, the algorithm is relatively insensitive to the scaling parameter by using the distances between data points and their neighbor points to get the scaling parameter adaptively. Simulation experiment shows the effectiveness and superiority of the proposed algorithm.