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In order to solve the problems that the flight safety is affected by internal sediment, stagnant water and cracks caused by the damage of the outer skin of aeronautical honeycomb panels, electrical capacitance tomography (ECT) is proposed to detect the defects of honeycomb composite materials. In order to solve the problem of low accuracy of planar ECT imaging, by constructing multi-scale fusion strategy, residual coding and decoding fusion module, and introducing a new pooling module to form a deep neural network of multi-scale residual coding and decoding path, the final result is fully integrated with the characteristics learned in the decoding stage, and the reconstructed image using conjugate gradient imaging algorithm is further improved. The results show that the defect detection of honeycomb materials can be realized by using planar ECT technology, and the image reconstruction effect can be improved by multi-scale fusion honeycomb composite defect detection network, and the defect image of honeycomb structure can be reconstructed more clearly.