<![CDATA[The rapid expansion of e-commerce and fintech has significantly shaped the digital economy ecosystem in Indonesia. This study analyzes key trends, behavioral patterns, and the evolving dynamics within these sectors as digital adoption continues to accelerate. The increasing volume and complexity of digital transactions demand advanced analytical approaches capable of identifying hidden patterns and potential anomalies. To address this need, the study employs a Convolutional Neural Network (CNN) model to extract deep feature representations from transaction data and classify emerging behavioral trends. The proposed method demonstrates strong accuracy, achieving 91.25%, indicating its ability to capture non-linear relationships that traditional methods often overlook. The findings highlight several major trends, including shifting consumer behavior, increasing transaction frequency, and the growing prominence of digital financial services. Practically, this research provides valuable insights for enhancing risk mitigation, fraud detection, and real-time monitoring in digital platforms. Academically, it contributes to the understanding of deep learning applications in digital economic analysis and opens avenues for further research on hybrid models and multi-source data integration within the digital economy ecosystem.]]>
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