Discriminant characteristics of brain functional connectivity can be used as a biomarker for the diagnosis of neuropsychiatric diseases. Using a machine learning method to identify is an important topic in brain science research. Most of the existing recognition methods of brain functional connectivity biomarkers ignore the impact of the characteristics of high-dimensional, continuous and multi noise of brain functional connectivity data on the recognition performance, resulting in the weak classification ability of the obtained biomarkers. This paper proposes a brain function connectivity biomarker recognition method based on neighborhood decision rough sets. Firstly, according to the characteristics of continuity and high noise of brain function connectivity data, a neighborhood decision rough set, which can effectively deal with continuous and high noise data, is introduced to identify brain functional connectivity discriminant features with stronger classification ability as biomarkers. Then, according to the high-dimensional characteristics of brain function connectivity data, the efficiency of identifying brain function connectivity biomarkers by the neighborhood decision rough set is guaranteed by quickly generating the neighborhood and reducing the feature search space. The experimental results on the ABIDE I and ADNI data sets show that the proposed method can accurately and quickly obtain the discriminant features of brain functional connectivity with stronger classification ability, and is expected to provide more accurate biomarkers for the diagnosis of neuropsychiatric diseases.