Research an efficient data gathering method to reduce the energy consumption of the Chinese medicine wearable devices and prolong the life-cycle of the Chinese medicine wearable device networks. Methods: This study intends to adopt G-Means method to divide the devices into multiple clusters through the positions of the devices while the devices obey a Gaussian distribution. Then the study intends to assign the weights for all the devices of the clusters based on the residual energy and the positions, and use the weights to select appropriate cluster heads for each cluster. The selected cluster heads collect the data from the devices of the corresponding cluster and transmit the data to the sink node. Results: The average energy consumption for all nodes of the first 100 rounds in scenario 1 and scenario 2 is 0.0262J and 0.0555J, respectively, which is more than 10% lower than the same type of methods. The failure time of the first node in scenario 1 and scenario 2 of the proposed method is the 910th round and the 849th round, which is more than 10% longer than the same type of methods. Conclusion: The proposed efficient data gathering method for the Chinese medicine wearable devices can optimize the topology of the Chinese medicine wearable device networks, enhance the energy efficiency of the devices and extend the network life-cycle.