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Although remote sensing image target detection is an extremely effective means of monitoring land surface changes, it is extremely susceptible to the complexity of the natural environment, resulting in mixed impurity pixels in remote sensing images, resulting in poor target detection accuracy. To solve this problem, a remote sensing image target detection system based on deep learning is designed. Establish a deep learning framework, connect the remote sensing image input module, image frame preprocessing module and target detection algorithm module at different levels, and then integrate the obtained remote sensing image pixel data with the help of the image target output structure unit to realize the system hardware design. On this basis, the multi-feature conditions of remote sensing images are extracted, and the existing target detection system design scheme is improved. By dividing multi-level target nodes, the wavelet decomposition results of remote sensing image characteristics are obtained, and the edge texture coefficients obtained by calculation are used to realize the detection of remote sensing image target change ability fused with deep learning theory. The experimental results show that the designed remote sensing image target detection system can effectively eliminate the amount of impurity pixels, can adapt to the complex and changeable natural environment, and obtain more accurate surface change monitoring results.