In order to deal with various complex telemetry problems, such as high dimensionality, huge volume, unbalanced categories and failure to intuitively make sense about states of payload, and considering the requirement of interpretability in space mission, a general method for fast identification of payload based on information gain and integrated learning method was proposed. Sample statistics and information gain was used to select features and reduce the dimension of the telemetry data; meanwhile, the integrated learning algorithm was used to complete the adaptive recognition and classification about payload states. The proposed method combined the advantages of the parameter classification information evaluation criteria of the information gain and strong modeling, high accuracy and strong anti-noise ability under unbalanced category samples. Furthermore, the model had to possess the property of being explanatory and able to find the key parameters. The method was verified by experiments using actual mission data, which was tested using the payload telemetry data on operational scientific satellite mission. Following that, an state-of-art result, of which the overall recognition accuracy is higher than 90 percent and a few samples can also be identified, covered mission requirement in all and proved the effectiveness and practicability.