In recent years, conventional chemistry techniques have faced significant challenges due to their inherent limitations, struggling to cope with the increasing complexity of and volume of data generated in contemporary research endeavors. Computational methodologies represent robust tools in the field of chemistry, offering the capacity to harness potent machine learning (ML) models to yield insightful analytical outcomes. This review examines the integration of machine learning into natural product chemistry from 2015 to 2023, highlighting its potential to overcome the inherent limitations of traditional chemical techniques. We present a structured approach that matches specific natural product challenges—such as component determination, concentration prediction, and classification—with suitable ML models, including regression, classification, and dimension reduction methods. Our objective is to illustrate how ML pipelines, from data preprocessing to model evaluation, enhance both qualitative and quantitative analyses, providing a comprehensive framework, with the potential catalyze a transformation in the field of natural product analysis.

As the complexity and volatility of global financial markets increase, traditional market forecasting and risk control methods can no longer effectively cope with the various uncertainties in the modern financial environment. Therefore, financial market prediction and risk control based on deep reinforcement learning has become a research hotspot. This paper proposes a market turbulence prediction and risk control model based on the improved A3C algorithm, aiming to improve the accuracy of market volatility prediction and the precision of risk control through reinforcement learning methods. Through experimental verification on multiple public financial data sets, the model proposed in this paper is superior to traditional reinforcement learning algorithms such as Q-learning, DQN, and PPO in terms of prediction accuracy, risk control accuracy, and convergence speed, showing strong learning ability and adaptability. In terms of model design, the parallel mechanism and advantage function are combined to significantly improve the training efficiency and model stability. Experimental results show that the model proposed in this paper can not only effectively capture the patterns of market turbulence but also adjust strategies according to real-time market data, thereby achieving effective control of risks. In addition, this paper also analyzes the contribution of each module to the model performance through ablation experiments, further verifying the effectiveness and advantages of the model. Finally, this paper looks forward to future research on market forecasting and risk control technology based on deep reinforcement learning. It is believed that with the continuous advancement of technology, AI-based financial risk management will play an increasingly important role in practical applications. It has become a powerful tool to enhance the stability of financial markets and respond to unexpected market risks.

With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data and machine learning. By constructing a four-layer architecture, it effectively integrates large-scale financial data and advanced machine learning algorithms. Key technologies employed in the system include Long Short-Term Memory (LSTM) networks, Random Forest, Gradient Boosting Trees, and real-time data processing platform Apache Flink, ensuring the real-time and accurate nature of risk monitoring. Research findings demonstrate that the system significantly enhances efficiency and accuracy in risk management, particularly excelling in identifying and warning against market crash risks.