Identifying important features from high-dimensional complex industrial data is crucial for production process anomaly monitoring. Aiming at the problem that the existing feature selection algorithms are difficult to model the complex intrinsic structure of data in the face of noise disturbance， a Multi-view Consistency-driven Robust feature selection method （MCR） was proposed. Firstly， a consistency-guided denoising mechanism with structure preservation was designed， in which multi-view collaborative modeling and inconsistency region detection were used to eliminate local noise disturbance while improving structural fidelity and integrity of the raw data. Then， a joint discriminative and consistency-driven feature fusion module was constructed， where high-quality multi-view embedding representations and a feature weight matrix were learned simultaneously， thereby enhancing the ability to perceive key feature dimensions. Finally， a cooperative sparse regularization-based feature selection strategy was introduced， so as to select the most discriminative and structurally consistent subset of features from the fused embedding space. Without relying on labeled information， this method achieves perception and selection of key feature dimensions through multi-view collaborative modeling and consistency-driven optimization. Extensive experimental results on several public benchmark datasets and a real-world cigarette production dataset demonstrate that MCR outperforms the existing mainstream feature selection methods such as Binary Horse herd Optimization Algorithm （BinHOA） and Improved Binary DJaya Algorithm （IBJA）， achieving classification accuracy improvements of 0.23 to 12.15 percentage points on public datasets and 2.22 to 5.00 percentage points on real industrial dataset， validating its robustness and effectiveness in complex scenarios.