Replacing expensive objective function evaluations with computationally inexpensive surrogate models to assist evolutionary algorithms in solving expensive black-box multi-objective optimization problems has garnered significant attention in recent years. Model accuracy plays a critical role in surrogate-assisted Multi-Objective Evolutionary Algorithms （MOEAs）； particularly when dealing with numerous objective functions， inaccurate models may misguide the search direction. However， due to the high cost of objective function evaluation， obtaining sufficient training samples to build high-quality surrogate models remains challenging. To address this issue， a Two-stage Infill Sampling-based Semi-supervised Expensive Multi-objective Optimization Algorithm （TISS-EMOA） was proposed. Semi-supervised techniques were introduced to augment the training dataset by selecting partial unlabeled data， thereby improving model accuracy. Simultaneously， a two-stage infill sampling criterion was introduced to acquire high-quality solutions for expensive multi-objective optimization problems under limited evaluation budgets. To validate the effectiveness of TISS-EMOA， experiments were conducted on the DTLZ1 - DTLZ7 benchmark problems and a real-world vehicle frontal structure optimization design. Compared with five State-Of-The-Art （SOTA） surrogate-assisted multi-objective evolutionary algorithms， TISS-EMOA achieves 25， 28， 28，24， 23 optimal or equal Modified Inverted Generational Distance （IGD⁺） results in 28 benchmark problems.