The material science literature contains up-to-date and comprehensive scientific knowledge of materials. However, their content is unstructured and diverse, resulting in a significant gap in providing sufficient information for material design and synthesis. To this end, we used natural language processing (NLP) and computer vision (CV) techniques based on convolutional neural networks (CNN) to discover valuable experimental-based information about nanomaterials information and synthesis methods in energy-material-related publications. Our first system, TextMaster, extracts opinions from texts and classifies them into challenges and opportunities, achieving 94% and 92% accuracy, respectively. Our second system, GraphMaster, realizes data extraction of tables and figures from publications with 98.3% classification accuracy and 4.3% data extraction mean square error on average. Our results show that these systems could assess the suitability of materials for a certain application by evaluation of real synthesis insights and case analysis with detailed references. This work offers a fresh perspective on mining and unifying knowledge from scientific literature, providing a wide swatch to accelerate nanomaterial research through CNN.