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Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field. [ABSTRACT FROM AUTHOR]