In response to challenges such as scale variation， background interference， and partial occlusion in crowd counting within complex scenes， a DA-DCCNN （Dual Attention based Dilated Contextual Convolutional Neural Network） was proposed. Firstly， the convolutional layers from VGG16 were utilized as feature extractors to obtain abstract and deep-level feature maps of the crowd image. Subsequently， by employing dilated convolutions， a Dilated Context Module （DCM） was constructed to connect features obtained from different layers. The Spatial Attention Module （SAM） and Channel Attention Module （CAM） were introduced to acquire contextual information. Finally， a loss function was formulated by combining the Euclidean distance and cross entropy to measure the disparity between the predicted attention map and the ground truth attention map. Experimental results on three publicly available datasets — ShanghaiTech， UCF_CC_50 and UCF-QNRF demonstrate that DA-DCCNN can effectively capture multi-scale features in the image and enhance the perception of important regions and channels within the image， achieving the optimal Mean Absolute Error （MAE）. The feature fusion network based on dual attention mechanism can efficiently recognize spatial structures and local features in images so that by using the generated density maps， the crowd regions can be predicted and counted more accurately.