Submodular　maximization　is　one　of　the　central　topics　in　combinatorial　optimization.　It　has　found　numerous　applications　in　the　real　world.　In　the　past　decades,　a　series　of　algorithms　have　been　proposed　for　this　problem.　However,　most　of　the　state-of-the-art　algorithms　are　randomized.　There　remain　non-negligible　gaps　with　respect　to　approximation　ratios　between　deterministic　and　randomized　algorithms　in　submodular　maximization.　In　this　paper,　we　propose　deterministic　algorithms　with　improved　approximation　ratios　for　non-monotone　submodular　maximization.　Specifically,　for　the　matroid　constraint,　we　provide　a　deterministic　0.283−o(1)　approximation　algorithm,　while　the　previous　best　deterministic　algorithm　only　achieves　a　1/4　approximation　ratio.　For　the　knapsack　constraint,　we　provide　a　deterministic　1/4　approximation　algorithm,　while　the　previous　best　deterministic　algorithm　only　achieves　a　1/6　approximation　ratio.　For　the　linear　packing　constraints　with　large　widths,　we　provide　a　deterministic　1/6−ϵ　approximation　algorithm.　To　the　best　of　our　knowledge,　there　is　currently　no　deterministic　approximation　algorithm　for　the　constraints.　©　2023　Elsevier　B.V.