The methods based on artificially designed features cannot extract high-level information from remote sensing images and previously used Convolutional Neural Network （CNN） such as VGGNet and ResNet cannot focus on distinguishable classification features in remote sensing images. In order to solve the problems， a novel method called ECA-ResNeXt-8-SVM was proposed based on Effective Channel Attention （ECA） mechanism for remote sensing image scene classification. In order to build an effective model， a deep feature extraction network called ECA-ResNeXt-8 embedded with the ECA module was designed， and the end-to-end learning was used to make network lay emphasis on channels with distinguishable classification features. At the same time， Support Vector Machine （SVM） was utilized to replace the fully connected layer as the classifier of the extracted deep features， which helped to improve the classification accuracy and generalization ability of model. On the experimental dataset UC Merced Land-Use， the classification accuracy of the proposed model reaches 95.81%， which is increased by 6% and 18% compared to SE-ResNeXt50 and ResNeXt50 networks respectively. When the classification accuracy is 75%， the proposed model has the training time reduced by 82% and 81% compared to the two above networks respectively. Experimental results show that the proposed model can reduce the convergence time of model effectively and improve the classification accuracy for remote sensing image scene.