The study classifies 1,200 dance frames with a SENet backbone optimized by the Beluga Whale Optimization Algorithm. The approach boosts accuracy by 12 percentage points over standard CNNs. Researchers suggest the model can streamline choreography analysis for dance studios. Practitioners can adopt the framework to automate scene labeling in performance recordings.