Intelligent computing algorithm optimization in large-scale text data mining

To address performance bottlenecks in large-scale text data mining, this paper proposes a multi-dimensional optimization strategy. Using an 8-node Spark cluster, parallel computing achieves processing speeds of 12,000 samples/second. Feature hashing reduces memory usage from 24GB to 3.2GB, while knowledge distillation compresses the model to 40% of its original size while maintaining 93.5% accuracy. Experiments on three datasets, including Weibo sentiment analysis, demonstrate the effectiveness of this optimization scheme for TB-scale text processing.