Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients’ interests well with effective treatment, which is important to them and their family. Studies have investigated the resting-state functional magnetic resonance imaging (rsfMRI) to characterize the abnormal brain function by computing the voxel-wise measures and Pearson’s correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. It is still a challenging problem to explore the powerful measures of rsfMRI to improve ADHD diagnosis. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based HOFC (tHOFC) and dynamics-based HOFC (dHOFC), as well as the sparse representation (SR)-derived FCs including the group SR (GSR), strength and similarity guided GSR (SSGSR) and sparse low-rank (SLR). Finally, these multiple measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is tested with the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to the single measure and the previous studies. We have also identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.