In this paper, a new evaluation method based on a multi-feature indicator decision tree is proposed to address the complexity in weight allocation for evaluation metrics and the flexibility of algorithm development platforms in camouflage effectiveness evaluation. The method selects five features, texture, color, brightness, structural similarity, and camouflage target size, as evaluation indicators based on visual attention mechanisms and trains a camouflage effectiveness evaluation model using a machine-learning decision tree classifier, which is ported to a small-sized, low-power Raspberry Pi development platform. Through the accuracy comparison experiment with two evaluation methods of mean weight method and entropy weight method, the accuracy of mean weight method is 56%, the accuracy rate of entropy weight method is 74%, and the proposed method achieves an accuracy of 90%. The real-time experiments demonstrate that the method can get the evaluation results of camouflage effect in about two seconds outside the field.