The invention discloses a clustering method based on an adjacent grid search strategy, and the method comprises the steps: firstly carrying out grid division of original data: enabling an original data set to be divided into a limited number of cells through a multi-dimensional space grid, and carrying out the denoising processing when necessary; secondly, performing grid clustering on the divided data: processing the denoised grids by utilizing a halo threshold value, and dividing the denoised grids into halo cell elements and core cell elements; establishing an adjacent grid operator for quickly searching an adjacent cell element of one cell element; achieving a clustering process through two steps of core cell element clustering and halo cell element division, dividing all core cell elements into a plurality of class clusters through a traversal algorithm, and dividing halo cell elements into existing class clusters based on cell element distances; and finally, clustering optimization is carried out according to data characteristics and user requirements. Compared with the prior art, the method has the advantages that a new clustering method can be provided for rapidly increasing the dimension of the sample set, and class clusters with complex boundary shapes can be effectively identified.