Precise condition assessment of brushless direct current motors (BLDCM), as critical power units in industrial drive systems, is essential for ensuring their safe and stable operation. However, insulation degradation between adjacent stator windings can lead to inter-turn short circuit(ITSC). Early-stage ITSC present significant challenges, including spatially scattered and highly redundant characteristic information, posing new difficulties for equipment evaluation. To address these issues, this paper proposes an intelligent ITSC identification method for inter-turn short circuits in BLDCM based on electromagnetic field feature distillation. Firstly, an electromagnetic equivalent model is established to analyze the physical relationship between leakage flux signals and winding states, revealing the nonlinear law governing the spatial redistribution of leakage flux relative to the number of shorted turns. Secondly, multi-source spatial leakage flux signals are fused using recursive phase space reconstruction techniques to construct high-information-density electromagnetic field feature maps, overcoming the limitations of traditional single-dimension analysis.Finally, an augmented training set covering diverse noise scenarios is constructed to significantly improve the model′s robustness in complex industrial environments. Through the synergistic optimization of multi-source feature fusion, lightweight architecture, and anti-interference strategies, this research provides an innovative solution for highly reliable online monitoring of motors in industrial settings.