Deep Learning Algorithm Framework

Economic Model Assessment:

• Application of XGBoost and decision tree algorithms to evaluate GameFi project parameters such as inflation rate, player retention, and reward mechanisms, producing a health score (0–100) to detect Ponzi-like structures or liquidity exhaustion risks.

• Combined use of LSTM and Transformer models to forecast asset price volatility, improving prediction accuracy by 40% compared to traditional time-series methods.

NFT Value Discovery:

• Utilizes computer vision and natural language processing (NLP) to analyze NFT scarcity (e.g., attribute combinations, mint supply), community consensus (e.g., social media traction), and historical trading data to provide real-time valuation and liquidity recommendations.

• Reinforcement learning is employed to optimize NFT staking and lending strategies, dynamically adjusting collateral ratios and interest rates to minimize liquidation risks.