This study proposes a genetic algorithm-enhanced dhole optimization algorithm coupled with a long short-term memory network (GA-DOA-LSTM) for dynamic identification of a high bypass ratio separate-flow turbofan engine during acceleration. A component-level JT9D model was implemented in T-MATS, and a dataset was generated at 54 flight operating points under step, ramp, and parabolic throttle excitations. Low- and high-pressure spool speeds, high-pressure compressor outlet total pressure, combustor exit temperature, and fuel flow were used as inputs, while low-pressure turbine exit temperature (T5) and thrust were predicted. GA-DOA combines dual-population coevolution, a global optimum pool, periodic elite exchange, greedy selection, and a final DOA refinement to jointly optimize the LSTM architecture and output loss weights. Compared with LSTM, PSO-LSTM, SSA-LSTM, DOA-LSTM, and GA-LSTM, the proposed model achieved RMSE, MAE, and MAPE values of 1.6121 K, 1.0136 K, and 0.1512% for T5 and 2430.10 N, 1832.24 N, and 1.2576% for thrust, respectively. The model maintained consistent transient tracking under different altitudes, Mach numbers, and throttle profiles, demonstrating its potential for aero-engine performance assessment, health monitoring, and control-oriented modeling. The main limitation is that the present validation remains simulation-based and engine-specific; experimental and cross-engine validation will therefore be required before practical deployment.