Recently, the evolution of deep learning has promoted the application of machine learning (ML) to various systems. However, there are ML systems, such as autonomous vehicles, that cause critical damage when they misclassify. Conversely, there are ML-specific attacks called adversarial attacks based on the characteristics of ML systems. For example, one type of adversarial attack is an evasion attack, which uses minute perturbations called "adversarial examples" to intentionally misclassify classifiers. Therefore, it is necessary to analyze the risk of ML-specific attacks in introducing ML base systems. In this study, we propose a quantitative evaluation method for analyzing the risk of evasion attacks using attack trees. The proposed method consists of the extension of the conventional attack tree to analyze evasion attacks and the systematic construction method of the extension. In the extension of the conventional attack tree, we introduce ML and conventional attack nodes to represent various characteristics of evasion attacks. In the systematic construction process, we propose a procedure to construct the attack tree. The procedure consists of three steps: (1) organizing information about attack methods in the literature to a matrix, (2) identifying evasion attack scenarios from methods in the matrix, and (3) constructing the attack tree from the identified scenarios using a pattern. Finally, we conducted experiments on three ML image recognition systems to demonstrate the versatility and effectiveness of our proposed method.