Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that affects children. However, the traditional scale-based diagnosis methods rely more on subjective experiences, leading to a demand of objective biomarkers and quantified diagnostic methods. This study proposes a quantitative approach for identifying ADHD tendency based on fingertip pressing force control paradigm with immersive visual feedback. By extracting nine behavioral features from reaction time and dynamic force fluctuation features with high temporal and amplitude resolution, the proposed method can effectively capture the continuous changes in attention levels for ADHD diagnosis. The extracted features were analyzed using independent sample t-test and Pearson correlation to determine their association with ADHD-RS scale scores. Results showed that 12 statistical indicators were effective for distinguishing ADHD children from typically developed children, and several features of force control ability were also associated with core ADHD symptoms. A support vector machine (SVM) based classifier is trained for ADHD diagnosis and achieved an accuracy of 78.5%. This work provides an objective and quantitative approach for identifying ADHD tendency within a short testing time, and reveals the inherent correlation between the attention levels and the extracted features of reaction time and force fluctuation dynamics.