<p>In this study, we propose a novel splitting algorithm designed to address a class of nonconvex composite optimization problems with linear constraints, characterized by a difference-of-functions structure. Our approach incorporates a relaxation step, linear modification, an inertial technique, and Bregman distance-based updates. This problem framework covers many important nonsmooth and nonconvex optimization problems in the literature, including difference-of-convex problems with linear constraints. Under mild assumptions, we prove that the sequence generated by our algorithm is bounded. Leveraging the Kurdyka-Lojasiewicz (KL) property, we further establish global convergence and derive the convergence rate. Finally, numerical experiments on the robust principal component analysis (PCA) problem demonstrate the efficiency of our method.</p>