A novel method integrating multi-feature weighted clustering and intelligent optimized neural network is proposed to address the issues of subjective feature weighting and insufficient nonlinear mapping accuracy in low-voltage distribution network line loss analysis. First, an improved k-means clustering algorithm ( MFW-IKCA: Multi-Feature Weighted Improved k-Means Clustering Algorithm) is established by introducing dynamic weight matrices and reference weights, combining entropy weighting and mutual information methods for adaptive feature weighting, and optimizing the objective function via the ADMM ( Alternating Direction Method of Multipliers). Subsequently, an IGA(Improved Genetic Algorithm) is employed to optimize the parameters of the LMBP-NNM ( Levenberg-Marquardt Backpropagation Neural Network ), enhancing convergence speed and prediction accuracy through simulated binary crossover, polynomial mutation, and adaptive damping factors.Finally, decision tree classifiers are utilized to extract clustering rules, while kernel density estimation enabled anomaly detection. Experimental results on 710 transformer district datasets have demonstrated a 14. 5% improvement in clustering purity, a 29. 3% reduction in MSE ( Msemean Squared Error ), and a 0. 892 coefficient of determination (R 2 ). This study provides a new perspective for refined line loss analysis and offers significant engineering value for improving scientific decision-making in loss reduction of distribution network.

line loss feature recognition, multi-feature weighted clustering, improved genetic algorithm, levenberg-marquardt neural network, dynamic weight allocation, distribution network line loss calculation