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The data were drawn from the China Longitudinal Aging Social Survey and the final valid sample was 10,703. A random forest classifier, a supervised machine learning method, was employed to identify the key influencing factors of elder abuse. Children\u2019s economic status is found to be the most important factor in predicting elder abuse, followed by the number of children, the health status of older people, intergenerational relations, children\u2019s time pressure, and the provision of home-based elderly care services. The likelihood of elder abuse decreases continuously with better economic status and less time pressure of children, better health of older people, more children, and better intergenerational relationships, whereas the influences of home-based elderly care services on elder abuse are not monotonous. Number of children contributes most to predict financial abuse, while children\u2019s economic status plays the most significant role in predicting physical and psychological abuse and neglect. This study is the first to apply a supervised machine learning approach with a random forest classifier for the identification of risk factors associated with elder abuse. 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