This　study　proposes　a　multi-fidelity　efficient　global　optimization　framework　for　the　structural　optimization　of　self-excited　oscillation　cavity.　To　construct　a　high-precision　multi-fidelity　surrogate　model　to　correlate　the　structural　parameters　of　a　self-excited　oscillation　cavity　with　the　gas　precipitation　and　energy　consumption　characteristics　by　effectively　fuzing　the　information　of　different　fidelity　levels,　choosing　different　correlation　functions　and　hyper-parameter　estimation　methods,　and　learning　the　correlation　between　the　data.　The　optimization　framework　determines　various　sampling　methods　and　quantities　by　calculating　the　minimum　Euclidean　distance　between　sample　points　and　sensitivity　index.　To　enhance　computational　efficiency,　a　multi-fidelity　sample　library　is　established　by　utilizing　both　precise　and　coarse　computational　fluid　dynamics　grids.　The　expected　improvement　criterion-based　algorithm　for　global　optimization　is　employed　as　an　additive　strategy　to　incorporate　additional　data　points　into　the　model.　This　approach　considers　both　local　and　global　search　of　the　model,　thereby　enhancing　sample　accuracy　while　reducing　computation　time.　Moreover,　the　utilization　of　the　highly　generalized　Non-dominated　Sorting　Genetic　Algorithm-II　(NSGA-II)　for　identifying　the　Pareto　optimal　solution　set　enhances　convergence　speed.　The　proposed　optimization　framework　in　this　study　achieves　a　remarkable　level　of　model　accuracy　and　provides　optimal　solutions　even　with　a　limited　sample　size.　It　can　be　widely　used　in　engineering　optimization　problems.