In recent years, advances in natural language processing (NLP) have increasingly relied on computational infrastructure, including hardware accelerators, scalable memory systems, software libraries, and framework, and widespread adoption of cloud platforms. However, existing entity recognition methods and scientific knowledge graphs largely overlook these components, instead focusing on research tasks, methods, datasets, and evaluation metrics. To address this gap, we present InfraKG, a large-scale Infrastructure Knowledge Graph that captures and links infrastructure-related entities mentioned in scientific publications. InfraKG is built using a hybrid information extraction framework applied to 85,000 arXiv papers in the computational linguistics domain, combining transformer-based NER models, semantic sentence filtering, and large language models (LLMs). The resulting graph contains 166,728 nodes and 1.5 million relations across seven types, connecting infrastructure entities to scientific publications along with their metadata. InfraKG is the first large-scale resource to systematically represent computational infrastructure in NLP research, enabling advanced queries, trend analysis, and infrastructure-aware literature reviews. We evaluated the proposed framework on 470 manually annotated PDF papers for infrastructure entities, covering a test set of 20,774 sentences. All code and data are publicly available at: code repository .