Based on the typical characteristics of natural ecosystems, the concept of robot ecosystem was proposed. Through the intelligent coordination and complex evolution of cluster robots, life features such as self-sustaining, self-replication and self-evolution emerged, enabling them to achieve long-term survival, reproduction and evolution under unmanned conditions, and perform specific tasks. According to the requirements of autonomous task decision-making in typical task scenarios of robot ecosystem, the characteristics of different machine learning task decision-making methods were analyzed, and the decision tree model and neural network model of autonomous task decision-making in robot ecosystem were established. The analysis shows that the accuracy of the two models is 80%~90%, and both have good stability. The results show that the autonomous task decision-making problem of robot ecosystem can be well solved by machine learning methods such as decision tree and neural network, so as to provide technical support for task application in unmanned scenes.