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The autoclave is a critical piece of equipment often used in the field of hydrometallurgy, presenting the risk of hazardous gas leaks. Additionally, such leaks could destabilize the pressure within the autoclave, potentially causing explosions that threaten both the equipment and production safety. Voice monitoring during autoclave gas leaks is a standard procedure. This paper proposes a high-frequency, high-order spatial interaction recognition algorithm for the voice of autoclave leaks. Firstly, low-frequency noise interference is eliminated from the recognition results using a high-pass filter. Next, a recursive gated convolutional block is employed to enable high-frequency components to interact in high-order spatial dimensions. Finally, a fully convolutional layer is utilized to recognize the sound of autoclave leaks. Experimental results demonstrate that the proposed algorithm achieves good recognition results for autoclave leaks, with an average confidence level of 0.93. When the confidence threshold is set at 0.65, the recognition accuracy can reach up to 99.5%.