Optimization Study of KNN Classification Algorithm on Large-Scale Datasets: Real-Time Optimization Strategy Based on Balanced KD Tree and Multi-threaded Parallel Computing

The K-Nearest Neighbors (KNN) algorithm is a classical supervised learning method widely used in classification and regression problems. However, the KNN algorithm faces serious challenges when dealing with high-dimensional large-scale datasets, mainly in terms of high computational complexity, low search efficiency, and difficulty in real time. In this study, an optimization strategy based on balanced KD tree and multithreaded parallel computing is proposed to address this problem. Experimental results show that the optimized KNN algorithm significantly improves the search speed while maintaining classification accuracy, realizes real-time classification of large-scale datasets, and provides a powerful tool for pattern recognition, data mining, and other fields in the era of big data.