Intelligent Decision-Making based on Computer Intelligent Algorithms and Big Data Technology

In the era of big data (BD), enterprises face challenges like data explosion and information complexity, making traditional decision-making methods inadequate. This paper proposes an intelligent decision-making model by integrating computer intelligent algorithms with BD technology, aiming to enhance efficiency and accuracy in dynamic data environments. It optimizes the Canopy-K-Gmedoids clustering algorithm and Interactive Genetic Algorithm (IGA) for better clustering accuracy and noise resistance, while lowering computational complexity and convergence time. The optimized Canopy-K-Gmedoids achieved 92.3% clustering accuracy and reduced computational time by 21.8%, whereas IGA improved convergence speed, cutting iteration numbers by 22.5%. Experimental analysis confirmed that BD technology significantly boosts corporate decision-making and management scores (p < 0.05). This research offers a novel technical approach for enterprise intelligent decision-making and paves the way for future studies.