As a scientific discipline, systems biology aims to build models of biological systems and processes through the computer analysis of a large amount of experimental data describing the behaviour of whole cells. It is within this context that we already developed the Biomolecular Network Ontology especially for the semantic understanding of the behaviour of complex biomolecular networks and their transittability. However, the challenge now is how to automatically populate it from a variety of biological documents. To this end, the target of this paper is to propose a new approach to automatically populate the Biomolecular Network Ontology and take advantage of the vast amount of biological knowledge expressed in heterogeneous unstructured data about complex biomolecular networks. Indeed, we have recently observed the emergence of deep learning techniques that provide significant and rapid progress in several domains, particularly in the process of deriving high-quality information from text. Despite its significant progress in recent years, deep learning is still not commonly used to populate ontologies. In this paper, we present a deep learning-based NLP ontology population system to populate the Biomolecular Network Ontology. Its originality is to jointly exploit deep learning and natural language processing techniques to identify, extract and classify new instances referring to the BNO ontology's concepts from textual data. The preliminary results highlight the efficiency of our proposal for ontology population.