Against the background of intensified price fluctuations and an overall oscillating upward trend in the metal market, traditional decision-making based on a fixed cut-off grade is difficult to adapt to market changes, which is detrimental to the long-term development of mining enterprises. Based on this, the internal logic and economic mechanism between metal price fluctuation and dynamic adjustment of mining cut-off grade were studied. Using the imperialist competitive algorithm (ICA), combined with models such as break-even analysis, multi-metal grade conversion, and stochastic price prediction, a logical framework for the dynamic adjustment of cut-off grade integrating data perception, model decision-making, intelligent execution, and feedback optimization was constructed. Taking a copper polymetallic mine as an example, four scenarios, namely static average price, progressive price increase, stochastic price path, and two-way price-cost linkage, were set up for empirical analysis. The results show that the cut-off grade is significantly negatively correlated with the price level, and dynamic adjustment can effectively prolong the mine production life and increase the net present value; the optimal strategy under stochastic prices is more conservative than that in deterministic scenarios, which can effectively mitigate the downside risk of metal prices; the cost elasticity coefficient has a significant regulating effect on the cut-off grade, and an increase in the cost elasticity coefficient reduces profit volatility while simultaneously weakening profit elasticity during periods of price increases. In the future, mining enterprises should focus on the deep integration of geological, market price and production data, and utilize intelligent technologies to achieve a fundamental transformation from "static design" to "dynamic response", thereby enhancing overall risk resistance capacity and resource utilization efficiency.