Aiming at the problem that the existing deep learning modulation recognition methods′ recognition rate is low under low SNR conditions and Insufficiently extracts and utilizes signal features, an Adaptive Wavelet and Multi-fusion Complex-value Dense Convolutional Neural Networks (AW-MCDCN) is proposed. The AW-MCDCN takes both IQ and AP signals as inputs, employing dense connections to construct a deep network that comprehensively extracts temporal features from IQ signals while incorporating AP signals to form heterogeneous feature complementarity. We further improve the classical complex-valued convolutional network by proposing a novel complex-valued cross convolution network based on complex convolution principles. Additionally, to resolve the excessive parameter quantity in traditional complex-valued networks, we embed a learnable wavelet decomposition layer that adaptively captures multi-scale signal features while incorporating frequencydomain characteristics. Experimental results demonstrate that our model achieves 98.31% peak recognition accuracy and 64.59% average accuracy on the RML2018.01a dataset, outperforming traditional network architectures by 1.65%~18.91% improvement margins, thus attaining SOTA performance.