In Japan, cashless is not yet popular but government and companies are devoted to the development of mobile payment methods. This research collected 241 Japanese users and applied decision trees algorithm. Six types of perceived risks (financial, privacy, performance, psychological, security, and time) were used and the categorized class is intention to use mobile payment (low, medium, and high). We also compared different competitive models to examine the performance, including decision trees, kNN, Naïve Bayes, SVM, and logistic regression and decision trees outperformed among all models. The findings indicated that privacy and performance risks are import to Japanese users. Safe, secured, reliable, and fast mobile payment environment are more important to low intention users (less concerns about financial risk). Financial loss, safe, secured, reliable, and fast mobile payment environment are more important to medium intention users (less concerns about time and security risk). Monetary loss, safe, reliable, and fast mobile payment environment are more important to high intention users (less concerns about security risk and psychological risk). The results can help Japanese companies unlock the perceived risk on mobile payment and furnish appropriate strategies to improve usage.

在日本，无现金尚未普及，但政府和企业致力于开发移动支付方式。本研究收集了 241 个日本用户并应用了决策树算法。使用六种类型的感知风险（财务、隐私、性能、心理、安全和时间），分类类别是使用移动支付的意图（低、中和高）。我们还比较了不同的竞争模型来检查性能，包括决策树、kNN、朴素贝叶斯、支持向量机以及逻辑回归和决策树，这些模型在所有模型中都表现出色。调查结果表明，隐私和性能风险对日本用户来说很重要。安全、可靠、快速的移动支付环境对低意愿用户更为重要（对财务风险的担忧较少）。财务损失，安全，有保障，可靠、快速的移动支付环境对中等意图的用户更为重要（对时间和安全风险的担忧较少）。金钱损失、安全、可靠、快速的移动支付环境对高意愿用户更重要（对安全风险和心理风险的担忧较少）。结果可以帮助日本公司解除移动支付的感知风险，并提供适当的策略来提高使用率。