Abstract: Investigating the material performance of fiber-reinforced concrete using mesoscale models is a highly important approach. To establish a mesoscale model of basalt fiber reinforced concrete (BFRC) that is closer to reality, a mesoscale modeling method capable of randomly generating aggregates and fibers was proposed. In this method, randomly sized aggregates were generated using convexity judgments, and fibers were generated using random endpoints and random angles. Simultaneously, a fast screening and interference detection based on circumscribed circles was conducted to ensure that the fibers did not penetrate the aggregates and the interfacial transition zone (ITZ). A two-dimensional mesoscale model of BFRC and a computational program were constructed following the workflow of "skeleton placement–geometry generation–phase identification–mesh mapping." The computational program was developed in Python, enabling the efficient batch generation and reproduction of the mesoscale models. Based on the established mesoscale model, the failure process of the BFRC specimens under uniaxial compression was simulated and comparatively analyzed with the experimental results. The results indicate that the established mesoscale numerical model can well simulate the uniaxial compression process of the specimens; specifically, the prediction of the load-bearing capacity of the BFRC is relatively accurate. The relative errors of the peak stress between the numerical simulations and the experimental results for the plain concrete and the BFRC (with a volume fraction of 1.0 vol%) are 0.50% and 0.29%, respectively. Furthermore, the data comparison reveals that the incorporation of the basalt fibers improves the compressive strength of the specimens by approximately 15.2% (from 51.73 MPa to 59.62 MPa), and the prediction error of the residual stress in the post-peak strain stage is controlled within 9.45%, which further verifies the reliability and rationality of the proposed mesoscale modeling method in characterizing the fiber toughening mechanism and model prediction.