While general-purpose Open Relation Extraction (ORE) systems have shown promise in textual processing, their direct application to Chinese fictional narratives encounters dual challenges: capturing intricate character interactions while fulfilling the domain-specific needs of literary analysis. In this paper, we present ORECC, an open relation extractor adapted from task-general ORE systems, specialized in capturing behavioral interactions between characters in Chinese novels. We propose a data augmentation scheme tailored to the features of novelistic texts and demands of literary studies to reconstruct the dataset. Trained on the reconstructed dataset, ORECC addresses the limitations of conventional single-span relation extractors by effectively extracting discontinuous relational strings and detecting null relational instances, and implements fuzzy processing of the extraction results. ORECC achieved 95.31% exact match accuracy in experiments, showcasing its viability as an analytical instrument for computational literary studies. Quantitative error analysis and the test on a specific Chinese novel also reveal the challenges faced by ORECC and illuminate the directions for our future efforts.