Cryptocurrencies have become a popular and widely researched topic of
interest in recent years for investors and scholars. In order to make informed
investment decisions, it is essential to comprehend the factors that impact
cryptocurrency prices and to identify risky cryptocurrencies. This paper
focuses on analyzing historical data and using artificial intelligence
algorithms on on-chain parameters to identify the factors affecting a
cryptocurrency's price and to find risky cryptocurrencies. We conducted an
analysis of historical cryptocurrencies' on-chain data and measured the
correlation between the price and other parameters. In addition, we used
clustering and classification in order to get a better understanding of a
cryptocurrency and classify it as risky or not. The analysis revealed that a
significant proportion of cryptocurrencies (39%) disappeared from the market,
while only a small fraction (10%) survived for more than 1000 days. Our
analysis revealed a significant negative correlation between cryptocurrency
price and maximum and total supply, as well as a weak positive correlation
between price and 24-hour trading volume. Moreover, we clustered
cryptocurrencies into five distinct groups using their on-chain parameters,
which provides investors with a more comprehensive understanding of a
cryptocurrency when compared to those clustered with it. Finally, by
implementing multiple classifiers to predict whether a cryptocurrency is risky
or not, we obtained the best f1-score of 76% using K-Nearest Neighbor.

通过分析历史数据和使用人工智能算法，本文研究了影响加密货币价格的因素以及如何识别风险的加密货币，并通过对链上参数的聚类和分类，得出了 39% 加密货币消失于市场而仅有 10% 存活超过 1000 天的结果，发现了加密货币价格与最大供应量、总供应量之间显著的负相关性，以及与 24 小时交易量之间的弱正相关性。同时，通过对加密货币的聚类，可以更全面地了解其特性，并通过 K - 最近邻算法得到 76% 的最佳 F1 分数，判断加密货币的风险程度。