The photonic Ising machine, a promising non-von Neumann computational paradigm, offers a feasible way to address combinatorial optimization problems. We develop a digital noise injection method for spatial photonic Ising machines based on smoothed analysis, where noise level acts as a parameter that quantifies the smoothness degree. Through experiments with 20736-node Max-Cut problems, we establish a stable performance within a smoothness degree of 0.04 to 0.07. Digital noise injection results in a 24% performance enhancement, showing a 73% improvement over heuristic Sahni–Gonzales (SG) algorithms. Furthermore, to address noise-induced instability concerns, we propose an optoelectronic co-optimization method for a more streamlined smoothing method with strong stability.