Abstract: Due to the limited communication distance of the nodes in the airborne LiDAR communication network, issues such as imbalanced hardware resources among ranging nodes, limited node energy, and communication interference exist. In response to these issues, a ranging data scheduling method for airborne LiDAR ranging based on balanced clustering is proposed. The collected radar ranging data is denoised using the nonlinear scale transformation structure in wavelet transform. Partitioning is performed using the K-means++ algorithm and a local search strategy, and the association features of different clustering results are analyzed using a density-based spatial clustering of applications with noise algorithm. An adaptive weight learning method is introduced to extract the feature quantities of the output radar communication network nodes, and the original feature vectors are fused with normalized node distribution quantized values to construct new network scheduling feature vectors for ranging data. Experimental test results show that, in the application of ranging node scheduling in radar communication networks, the proposed method has resulted in a reduction of memory usage to below 62 %, energy consumption to below 1 200 J, and execution time to below 40 ms, thereby enhancing the effectiveness of balanced clustering scheduling.