The Agile Earth Observation Satellite Scheduling Problem is a complex NP-hard challenge commonly tackled using heuristic algorithms. Traditional heuristic algorithms often exhibit low task completion rates and inefficiency, particularly in large-scale scenarios. This paper presents the red ocean heuristic algorithm, which improves both completion rates and computational efficiency through iterative decision-making, modeled on trader behavior. Experimental results show that our algorithm outperforms traditional heuristic, achieving a 12.13% improvement in completion rate and a 66.93% reduction in computation time.