This paper conducts the first systematic analysis of wash trading in four popular NFT markets, examining over 25 million transactions. The authors propose a heuristic algorithm that integrates network characteristics and behavioral analysis to detect wash trading activities. Key findings include: 1. Markets with incentivized structures, such as LooksRare and X2Y2, exhibit higher proportions of wash trading volume, with wash trade volumes accounting for 94.5% and 84.2% of total ETH volume, respectively. 2. The LooksRare market's reward system, which phases down daily LOOKS token rewards, initially increased wash trade volumes but later led to a significant decrease. 3. The study highlights the need for more nuanced regulatory frameworks and enhanced transparency to address the issue of wash trading, particularly in markets with reward mechanisms. The paper contributes to the understanding of wash trading in NFT markets and provides a method for detecting such activities using graph theory.This paper conducts the first systematic analysis of wash trading in four popular NFT markets, examining over 25 million transactions. The authors propose a heuristic algorithm that integrates network characteristics and behavioral analysis to detect wash trading activities. Key findings include: 1. Markets with incentivized structures, such as LooksRare and X2Y2, exhibit higher proportions of wash trading volume, with wash trade volumes accounting for 94.5% and 84.2% of total ETH volume, respectively. 2. The LooksRare market's reward system, which phases down daily LOOKS token rewards, initially increased wash trade volumes but later led to a significant decrease. 3. The study highlights the need for more nuanced regulatory frameworks and enhanced transparency to address the issue of wash trading, particularly in markets with reward mechanisms. The paper contributes to the understanding of wash trading in NFT markets and provides a method for detecting such activities using graph theory.