Traditional multi-label neighborhood rough sets treat all labels as a whole when calculating feature importance， failing to effectively distinguish the differences in contribution to feature selection among different labels and ignoring the noise interference caused by irrelevant labels. To address these issues， a Distributed Multi-Label feature selection method with Feature-label Neighborhood Collaborative Correlation （DML-FNCC） was proposed. Firstly， bidirectional spectral clustering was utilized to simultaneously mine the internal associations between labels and feature spaces： decision-representative primary label clusters were extracted in the label space to reduce noise interference， while a spectral clustering map based on semantic relevance was constructed in the feature space to achieve modular aggregation of semantically correlated features. Secondly， neighborhood dependency was employed to quantify the association degree between feature clusters and label clusters， selecting the feature subsets most closely related to each label cluster. Finally， a distributed framework was adopted to distribute computational tasks across multiple nodes， further accelerating the model training process. Experimental results on 12 public datasets demonstrate that DML-FNCC outperforms existing multi-label feature selection approaches， such as PMLFS （Partial Multi-Label Feature Selection） and WFDP （Weak-label Fuzzy Discernibility Pairs）. It achieves the top ranking in terms of average precision， Hamming loss， one error， ranking loss， and coverage， leading to improved classification performance.