Application of Intelligent Information Processing and Data Mining Systems in Innovation and Entrepreneurship

This paper proposes an innovation and entrepreneurship decision optimization system (IDEA-Miner) based on intelligent information processing and data mining, and designs an entrepreneurial opportunity mining algorithm optimized by reinforcement learning (RL-EntreOp), aiming to improve the accuracy of entrepreneurial opportunity identification and risk assessment capabilities. The IDEA-Miner system integrates social media, industry data, policies and regulations through the multidimensional data fusion method (MIMF), uses Transformer for data feature extraction, and uses the DDPG (deep deterministic policy gradient) reinforcement learning algorithm to dynamically optimize market trend prediction and entrepreneurial decision-making. The experiment is simulated based on real entrepreneurial financing data and market trend data, and compared with traditional methods such as SVM and LSTM. The experimental results show that the accuracy of RL-EntreOp in predicting the success rate of entrepreneurship reaches 89.7 %, the F1 value of market trend prediction increases to 0.81, and the return on investment (ROI) increases to 2.12, which are significantly better than traditional methods (SVM prediction accuracy 78.5 %, F1 value 0.67, ROI 1.45; LSTM prediction accuracy 82.3 %, F1 value 0.72, ROI 1.68). This study proves the effectiveness of the data mining system based on reinforcement learning in optimizing entrepreneurial decision-making, and provides entrepreneurs and investors with an intelligent and efficient decision support tool.