Design of financial data analysis and visualization system combining fuzzy c-means and convolutional neural network

With the growing volume and complexity of financial data, traditional analysis methods struggle to address high-dimensional, uncertain, and unstructured data effectively. This study proposes a novel hybrid model that combines fuzzy C-means (FCM) clustering with convolutional neural networks (CNN) to improve financial data analysis and prediction. FCM handles uncertainty through soft clustering, while CNN enables automatic deep feature extraction. The proposed system integrates data preprocessing, prediction, and visualization to support intelligent decision-making. Experimental results across multiple datasets show that the model outperforms traditional methods in terms of accuracy and stability. This work contributes a scalable, interpretable, and data-driven framework for financial analytics.