Specific biomarker reflecting neurobiological substrates of schizophrenia (SZ) is required for its diagnosis and treatment selection of SZ. Evidence from neuroimaging has implicated disrupted functional connectivity in the pathophysiology. We aimed to develop and validate a method of disease definition for SZ by resting-state functional connectivity using radiomics strategy. This study included 2 data sets collected with different scanners. A total of 108 first-episode SZ patients and 121 healthy controls (HCs) participated in the current study, among which 80% patients and HCs (n = 183) and 20% (n = 46) were selected for training and testing in intra-data set validation and 1 of the 2 data sets was selected for training and the other for testing in inter-data set validation, respectively. Functional connectivity was calculated for both groups, features were selected by Least Absolute Shrinkage and Selection Operator (LASSO) method, and the clinical utility of its features and the generalizability of effects across samples were assessed using machine learning by training and validating multivariate classifiers in the independent samples. We found that the accuracy of intra-data set training was 87.09% for diagnosing SZ patients by applying functional connectivity features, with a validation in the independent replication data set (accuracy = 82.61%). The inter-data set validation further confirmed the disease definition by functional connectivity features (accuracy = 83.15% for training and 80.07% for testing). Our findings demonstrate a valid radiomics approach by functional connectivity to diagnose SZ, which is helpful to facilitate objective SZ individualized diagnosis using quantitative and specific functional connectivity biomarker.