To address challenges including rule optimization difficulty, complex parameter tuning, and poor multi-scenario adaptability in existing fuzzy logic evaluation methods for complex industrial applications, this paper proposes an intelligent assessment system based on double-optimized fuzzy inference. First, a double optimization fuzzy inference system is designed, integrating fuzzy entropy-guided rule generation with fuzzy gradient collaborative descent to alternately optimize model structure and parameters, coordinated dynamically by an adaptive optimization scheduler. Second, a multi-scale fuzzy feature extraction network and adaptive fuzzy feature fusion mechanism are constructed, where the former extracts multi-granularity fuzzy features through parallel multi-scale branches while the latter achieves intelligent feature fusion via fuzzy channel-spatial co-attention. Finally, a dynamic fuzzy weighting allocation is proposed, employing a scene-aware weight generation network to dynamically adjust fuzzy rule weights based on input features. Validated in natural gas pipeline risk assessment and electrical equipment identification scenarios, experimental results demonstrate 95.83% assessment accuracy,for pipelines, and 96.54% accuracy with 96.32% F1-score for equipment identification. Compared to conventional fuzzy logic and deep learning methods, the proposed approach significantly enhances evaluation accuracy and generalization capabilities.