Neurofeedback training (NFT) is a noninvasive neuromodulation method for children with attention-deficit/hyperactivity disorder (ADHD). Brain rhythms, the unique pattern in electroencephalogram (EEG), are widely used as the training target. Most of current studies used a fixed frequency division of brain rhythms, which ignores the individual developmental difference of each child. In this study, we validated the feasibility of NFT using individual beta rhythm. A total of 55 children with ADHD were divided into two groups using the relative power of individual or fixed beta rhythms as the training index. ADHD rating scale (ADHD-RS) was completed before and after NFT, and the EEG and behavioral features were extracted during the training process. After intervention, the attention ability of both groups was significantly improved, showing a significant increase in beta power, a decrease in scores of ADHD-RS and an improvement in behavioral and other EEG features. The training effect was significantly better with individualized beta training, showing more improvement in ADHD-RS scores. Furthermore, the distribution of brain rhythms moved towards high frequency after intervention. This study demonstrates the effectiveness of NFT based on individual beta rhythm for the intervention of children with ADHD. When designing a NFT protocol and the corresponding data analysis process, an individualized brain rhythm division should be applied to reflect the actual brain state and to accurately evaluate the effect of NFT.