In the traditional acoustic emission valve internal leakage detection scenario, the portable inspection type instrument detection exists problems such as lack of real-time, low efficiency of data storage and management, and limited environmental adaptability, while the wireless acquisition and cloud processing are constrained by the battery life, and the cost of cloud computing power. To address the above problems, we propose an edge-cloud collaborative acoustic emission signal recognition method for valve internal leakage. Firstly, a lightweight recognition model is constructed, and residual blocks and multiple attention mechanisms are introduced in the complex frequency domain to adaptively focus on the global relationship between different frequency components and enhance the model’s ability to focus on key features. Deep convolution is used in the residual structure, and dimensional splitting of K and V is done in the attention mechanism to realize the compressed attention mechanism, so as to ensure the model lightweight. After mapping back to the time domain, the original input is summed with the reconstructed signal in the frequency domain to avoid information loss during frequency domain processing and to alleviate the problem of gradient vanishing. The encoder, decoder and recognition model are trained together in the training phase, the encoder is deployed in the wireless detection device to reduce the power consumption of wireless transmission in the deployment phase, and the decoder and recognition model are deployed in the cloud. The experimental results demonstrate that the proposed neural network model requires a mere 10.1×103 parameters to achieve optimal performance. This method, when implemented with a compression ratio of 8, reduces the accuracy from 99.5% to 98.9%, while concurrently reducing the energy consumption of the device from 0.49 mAh to 0.15 mAh. This enhancement not only prolongs the battery’s operational lifespan but also facilitates the enhancement of the detection frequency. This solution offers a cost-effective approach for the online monitoring and identification of leakage in valves.