There are some problems in results of the neural style transfer algorithms， such as artifacts， color loss， and blurred contour， which are negative to the overall artistic effect. Therefore， a neural style transfer algorithm based on Laplacian operator and Color Retention （LCR） was proposed. Content loss term， style loss， histogram loss， and Laplacian loss are utilized in the proposed LCR algorithm to construct the total loss function. Because histogram loss and Laplacian loss are used in the LCR algorithm， the proposed algorithm has better overall artistic effect on the stylized result images than Image Style Transfer using Convolutional Neural Networks （IST-CNN） algorithm and Deep Feature Perturbation （DFP） algorithm. Firstly， the influence of image noise on latter calculation of each loss was reduced by denoising input content image and style image. Secondly， the separation of image brightness channel L and color channel a， b was achieved by converting content image and style image from RGB space to Lab space. And the brightness information of content image was transferred to style image to preserve the color of content image. Finally， in Convolutional Neural Network （CNN）， the total loss function was iteratively optimized， and then the stylized result image was output. Compared with IST-CNN and DFP algorithms， the proposed LCR algorithm has the Peak Signal-to-Noise Ratio （PSNR） improved by 12.418 dB and 8.038 dB approximately and respectively， the Structural SIMilarity （SSIM） improved by about 0.348 06 and 0.258 54 approximately and respectively， and the Mean Square Error （MSE） decreased by about 0.653 76 and 0.296 00 respectively. Experimental results show that LCR algorithm has advantage in the overall visual effect of stylized drawing.