Action quality assessment (AQA) is vital in sports science and clinical rehabilitation, but existing methods suffer from data noise sensitivity, weak physical interpretability, and limited generalization across body types. This study proposes a novel AQA paradigm for standing long jump to address these limitations. A methodological framework based on kinematic reconstruction and physical deviation analysis is developed. Ideal trajectories derived from forward kinematics establish a unified evaluation benchmark. Three deviation features (Peak Deviation, Cumulative Deviation Integral, Phase-Weighted Deviation) are computed between ideal and experimental samples, then input into a Random Forest Classifier for automatic grading. Experimental results demonstrate the proposed method significantly outperforms traditional dynamic-time-warping-based templates in accuracy and robustness. Ablation studies confirm kinematic trajectory benchmarks are superior to raw or averaged trajectories. Model decisions show strong consistency with physical characteristics, verifying interpretable assessment capability. This work shifts AQA from “data similarity comparison” to “physical rationality evaluation”, offering a new perspective for vision-based automated motion analysis.