In contemporary industrial machinery, bearings are a vital component, so the ability to diagnose bearing faults is extremely important. Current methodologies face challenges in feature extraction and perform suboptimally in environments with high noise levels. This paper proposes an enhanced, multimodal, feature-fusion-bearing fault diagnosis model. Integrating a 1DCNN-dual MLP framework with an enhanced two-way cross-attention mechanism enables in-depth feature fusion. Firstly, the raw fault time-series data undergo fast Fourier transform (FFT). Then, the original time-series data are input into a multi-layer perceptron (MLP) and a one-dimensional convolutional neural network (1DCNN) model. The frequency-domain data are then entered into the other multi-layer perceptron (MLP) model to extract deep features in both the time and frequency domains. These features are then fed into a serial bidirectional cross-attention mechanism for feature fusion. At the same time, a GCOS learning rate scheduler has been developed to automatically adjust the learning rate. Following fifteen independent experiments on the Case Western Reserve University bearing dataset, the fusion model achieved an average accuracy rate of 99.83%. Even in a high-noise environment (0 dB), the model achieved an accuracy rate of 90.66%, indicating its ability to perform well under such conditions. Its accuracy remains at 86.73%, even under 0 dB noise and variable operating conditions, fully demonstrating its exceptional robustness.