<p>This paper presents a novel deep learning-based multi-parameter coupling compensation algorithm for clamp-on gas metering systems to address the complex interdependencies between temperature, pressure, and density variations that significantly affect measurement accuracy. Traditional linear and polynomial compensation methods fail to capture the nonlinear coupling effects between environmental parameters, leading to substantial measurement errors under dynamic operating conditions. The proposed approach employs a hybrid LSTM-CNN neural network architecture that simultaneously models temporal dependencies and spatial relationships within the multi-parameter space, enabling more accurate compensation compared to conventional methods. The algorithm incorporates real-time adaptive correction mechanisms with sliding window processing and dynamic weight adjustment to automatically respond to changing operating conditions. Experimental validation demonstrates significant performance improvements, achieving 0.52% average measurement error compared to 2.45% for conventional linear compensation, representing a 78% accuracy enhancement. Long-term stability testing confirms consistent performance over 720-hour continuous operation with 5.34 millisecond real-time processing capability suitable for industrial implementation. The research contributes to advancing precision gas flow measurement technology by providing a practical solution for achieving high-accuracy measurements under complex industrial operating conditions.</p>