Currently, the subcarrier modulation recognition methods in non-cooperative multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems suffer from insufficient recognition accuracy under low signal-to-noise ratio (SNR) conditions and limitations in recognizing higherorder modulation schemes. To address these issues, this paper proposes a modulation recognition algorithm based on feature fusion. First, the received signal undergoes preprocessing. Subsequently, the in-phase and quadrature (I/Q) components of the signal are extracted, and multiple features—including wavelet transform, fourth-power spectrum, higher-order cumulants, and zero-centered normalized instantaneous amplitude—are computed as input features. These features are then fed into a neural network for training. Finally, the modulation scheme of the MIMO-OFDM subcarriers is classified. Experimental results demonstrate that the proposed algorithm effectively recognizes six modulation types: BPSK, QPSK, 8PSK, 16QAM, 64QAM and 128QAM, achieving a recognition accuracy of 90% at an SNR of 6 dB.