Surrogate-assisted multi-objective evolutionary algorithms (SAEAs) have shown great performance in solving computationally expensive multi-objective optimization problems. However, when the dimensionality of the decision variables increases, the performance of these algorithms deteriorates drastically due to the curse of dimensionality and the interactions between the decision variables. In order to solve this problem, this paper proposes a fuzzy directed sampling assisted SAEA called FDSEMO. The use of directed sampling to obtain guiding solutions contributes to the rapid convergence of the population, and then using fuzzy operations to approximate the solutions in the population reduces the cumulative prediction error of the model and strengthens the global search capability. A comprehensive comparison of FDSEMO with six state-of-the-art algorithms on two standardized benchmarks shows that FDSEMO is capable of obtaining solution sets with better convergence and diversity.