Deep Hierarchical Hybrid Learning Framework for Autonomous Organizational Knowledge Mining and Productivity Forecasting

Abstract

Contemporary businesses produce great volumes of unstructured information in the form of emails, reports, meetings, and performance measurements. Conventional predictive models are not efficient in extracting latent insights as they do not have the ability of modelling cross modal information, causal reasoning and time restrictions. To overcome these limitations, this paper introduces Deep Hierarchical Hybrid Learning Framework, which provides a combination of 5 rare components: Hierarchical CoAttentional Embedding Networks to combine multimodal data; Inductive Graph Neural Networks that includes causal edge reasoning to construct knowledge graphs; Capsule Networks to model semantic intent; Neural Turing Machines to extract productivity signals through saliency-aware attention; and Deep Echo State Networks to make predictions. The model was tested using enterprise simulation data of 150 users in the past 12 months. The suggested framework reached an accuracy of 91.7% in intent classification, 86.5% F1 score in knowledge graph prediction, 89.7% in anomaly detection accuracy and 3.25 MAE in productivity predictions, which was better than the existing baselines such as BiLSTM, Transformer, and XGBoost. Besides realizing a high predictive accuracy, the system is also characterized by interpretability, generalization, and operational adaptability across the departments. This renders it appropriate to dynamic and decentralized enterprise settings that need autonomous knowledge mining and proactive decision support.