Tech-CNN: Deep Learning-based Framework with Stacked Sequence Encoding for Predicting the Growth Potential of Technologies

This paper presents an enhanced deep learning-based prediction framework, Tech-CNN, designed to predict the future trajectory of emerging technologies based on academic publication data. The framework integrates textual, citation, and research-area metadata and converts them into an h-year stacked sequence encoding to capture long-term growth trends while maintaining a dense and fixed-dimensional representation. The proposed framework is evaluated on a large-scale dataset comprising publications from 2006 to 2021 and achieves a prediction accuracy of approximately 88.7%. Compared to the previous method, the proposed framework improves the accuracy by 0.9 percentage points and f1-score by 7.2 percentage points. When applied to a recent dataset, the accuracy and f1-score were measured at 0.887 and 0.854, respectively. Comparative experiments with existing approaches demonstrate that Tech-CNN outperforms traditional methods in forecasting technology growth. Additionally, the framework effectively tracks the evolution of technologies, including the emergence and transformation of AI-based chatbot technologies into personalized intelligent agents. The results indicate that the proposed framework is robust and scalable, with potential applications in data-driven technology forecasting, trend analysis, and strategic R&D planning.