Sign language recognition has received widespread attention in recent years. However, existing sign language recognition models face challenges, such as long training times and high computational costs. To address this issue, this study proposes a hybrid deep learning method that integrates an attention mechanism with an Expanded Wide-kernel Deep Convolutional Neural Network (EWDCNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network based on data obtained from a wearable data glove, EWBiLSTM-ATT model. First, by widening the first convolutional layer, the model parameter count is reduced, which enhances computational speed. Subsequently, by deepening the EWDCNN convolutional layers, the model's ability to automatically extract features from sign language is improved. Second, BiLSTM is introduced as a temporal model to capture the dynamic temporal information of sign language sequential data, effectively handling temporal relationships in the sensor data. Finally, the attention mechanism is employed to map the weighted sum and learn a parameter matrix that assigns different weights to the hidden states of BiLSTM, allowing the model to automatically select key time segments related to gesture actions by calculating the attention weights for each time step. This study uses the STM32F103 as the main control module and builds a data glove sign language acquisition platform with MPU6050 and Flex Sensor 4.5 sensors as the core components. Sixteen dynamic sign language actions are selected to construct the GR-Dataset data training model. Under the same experimental conditions, compared to the CLT-net, CNN-GRU, CLA-net, and CNN-GRU-ATT models, the recognition rate of the EWBiLSTM-ATT model is 99.40%, which is increased by 10.36, 8.41, 3.87, and 3.05 percentage points, respectively. Further, the total training time is reduced to 57%, 61%, 55%, and 56% of the comparison models, respectively.