Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response. Display omitted •fastBCR enables fast clonal family inference from large-scale B cell repertoires•fastBCR is validated on simulated BCR datasets with diverse lineages and mutation rates•fastBCR identifies SARS-CoV-2-specific clonal families from COVID-19 patient samples The study of antibody repertoires and B cell activation is essential to understanding immune system function and developing effective treatments for various diseases. One important aspect of this research is identifying clonal families, which are groups of B cells that arise from a common ancestor and diversify through proliferation and somatic hypermutation. However, accurately and quickly clustering highly diverse clonally related sequences from large datasets remains challenging. To address this issue, we propose a heuristic method to rapidly and accurately infer clonal families from massive and diverse BCR sequences. Our method has been rigorously tested and shown to provide efficient and biologically meaningful results, contributing to a deeper understanding of B cell activation and antibody-related research. Wang et al. introduce a computational approach, fastBCR, for efficient BCR repertoire analysis. fastBCR accurately identifies B cell clonal families within varying simulated datasets. Application of fastBCR to COVID-19 patient data reveals disease-related features, demonstrating benefits in decoding disease-associated antibodies and understanding immune responses from BCR repertoires.