Water content plays a crucial role in the growth and metabolism of standing trees. Real-time and accurate measurement of water content is of key guiding significance for standing tree cultivation and forest management. A wood moisture content diagnosis system based on wireless acoustic emission sensor network (WASN) was designed and implemented for the nondestructive testing of living wood. Firstly, the acoustic emission signals of the trunk epidermis were sampled at high speed by the WASN node, and then the characteristic parameters were calculated and transmitted to the gateway wirelessly. After that, the optimal feature combination was selected by the MRMR criterion, and the water content identification model was established by the support vector machine (SSA-SVM) optimized by the sparrow algorithm. Finally, on-line real-time long-term monitoring and diagnosis can be carried out. The system has been tested on four species of met sequoia, poplar, pine and beech respectively, and the results show that the lowest diagnostic accuracy is 95. 5%. The design of WASN was fully capable of long-term observation of tree transpiration.