Some improvements and parallel computing studies were carried out with the Jacobi-Davidson(J-D) method. Some strategies, such as the spectral transformation technique, restart and deflation techniques, were added and integrated with the J-D method to make it suitable to the large-scale modal analysis. A parallel modal solving system based on PANDA framework was created using the improved J-D algorithm and various numerical software packages. Utilizing the solving system and parallel computers, the parallel scalability of the J-D algorithm was studied via numbers of tests on an engineering structure. The maximum computing scale is over 10 million degrees of freedom, and the maximum numbers of parallel CPU processors attain 128. The influences of inner iteration steps and numbers of restarted vectors on the convergence velocity of outer iterations were studied, and the speedup curves for different scales were obtained. The results show that the improved J-D method is competent for the large-scale modal analysis, the memory costs increase linearly with the computing scales and only 39.4 GB of memory is needed for the modal analysis of 10.25 million scales. Also, the improved J-D method takes on an excellent parallel scalability that the speedup curves are almost linear within 128 testing processors and the curve is gradually close to the ideal speedup one as the accretion of computing scales. The parallel efficiency of 10.25 million scales at 128 processors attain 88.1 %.