When dealing with large-scale multi-objective optimization problem (LSMOP), the MOEA/D algorithm shows poor scalability in the decision space and a tendency to converge to local optima as the dimensionality of decision variables increases. To address this issue, this study proposes a large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). The MSMOEA/D algorithm introduces a hybrid initialization strategy based on autoencoders in the optimization process to expand the coverage of the initial population, thus promoting global search. Moreover, a neighborhood adjustment strategy based on aggregation functions is proposed, which can more accurately control the search range during the search process by adjusting neighborhood sizes, thereby avoiding low search efficiency caused by excessively large or small neighborhoods. Furthermore, a mutation-selection strategy based on non-dominated sorting is adopted during the optimization process. Different subproblems select their mutation strategies according to the number of individuals in the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. Experimental results verify the effectiveness of the proposed algorithm for solving LSMOPs.