Abstract: In the context of surging global demand for mineral resources and an accelerated shift toward green, low-carbon development, the mining risk-exploration market—central to securing supplies of strategic minerals—faces multiple constraints, including limited factor mobility, pronounced information asymmetries, and insufficient policy support. Artificial intelligence（AI）, leveraging strengths in big-data analytics, risk forecasting, and intelligent decision-making, offers critical technological pathways to overcome these bottlenecks. Drawing on synergetics, this study systematically elucidates the evolutionary mechanisms and implementation pathways through which AI empowers the mining risk-exploration market. The analysis shows that the global risk-exploration landscape has coalesced around six major stock exchanges, with the Toronto Stock Exchange（TSX） and Australian Stock Exchange（ASX） bourses playing especially pivotal roles; across this architecture, AI is permeating the entire exploration value chain via intelligent pricing of resource assets, risk-prediction modeling, and innovations in ESG-linked financial instruments. By contrast, China’s risk-exploration market continues to be hampered by a weak data foundation, illiquidity of mineral rights, lagging policy and regulatory frameworks, shortages of interdisciplinary talent, and an underdeveloped intermediary-service ecosystem—factors that collectively constrain the efficacy of AI enablement. Mechanistically, AI empowerment follows the synergetic logic of “synergy formation-synergy processes-synergy performance.” Data integration and market demand first align heterogeneous actors around shared objectives; dynamic coupling among information, capital, and policy flows then generates a virtuous cycle, ultimately improving market efficiency, security, and sustainability. The underlying coordination mechanism can be summarized as a four-stage trajectory of “order-parameter activation coupling amplification-phase transition-steady state optimization”, where AI establishes data transparency, risk predictability, and intelligent pricing as the salient order parameters that drive the market’s transition from a fragmented, low-efficiency equilibrium to a securitized and digital, high-efficiency steady state. Correspondingly, it proposes the following implementation pathways and policy recommendations: at the infrastructure layer, build a sovereign AI mining-data system and computing platform, develop interpretable AI models; at the ecosystem layer, cultivate specialized service providers and industry-university-research alliances; and at the policy layer, enhance data openness and sharing, improve algorithm governance, expand green-finance support, and strengthen cross-border regulatory coordination—thereby establishing a robust institutional environment for AI-enabled transformation.