Behavioral observation plays an essential role in diagnosing Autism Spectrum Disorder (ASD) by analyzing children's atypical patterns in social activities (e.g., impaired social interaction, restricted interests, and repetitive behavior). To date, this process still heavily relies on the questionnaire survey, clinical observation, or retrospective video analysis, leading to high demand for professionals with massive labor costs. We propose a standardized platform for stimulating, gathering, analyzing, modeling, and interpreting human behavioral data in the application of AI-aided ASD diagnosis. By a structured assessment process, the proposed system can automatically evaluate children's multiple social interaction skills using the captured audio-visual data and provide the final diagnostic suggestions. We collected a multimodal behavioral database of 95 participants in a real clinical environment, the Third Affiliated Hospital of Sun Yat-sen University, China. On the clinical database, our proposed computer-aided ASD diagnosis system obtains an accuracy of 88.42% for identifying ASD children with an average age of 24 months, representing a performance comparable to top-level human experts. As a unified and replicable solution, it has good potential to be promoted to less developed areas with limited high-quality medical resources.