In order to improve the precision and efficiency of biochip sample application quality inspection on the production line, the work uses a method based on image processing and convolutional neural network to determine whether a biochip sample application quality is acceptable and inspect the radius of sample application point on the qualified biochip. The CCD camera was used to obtain the image after the biochip was sampled. Through image preprocessing, the image processing methods such as canny edge detection and round fitting were used to obtain the geometric information of the spotted point, and then the sample application point’s radius was calculated. At the same time, the detection method of sample application’s quality based on convolutional neural network was proposed. The convolution features of sample application points were extracted by the regional recommendation network, and the detection model was introduced by dividing the fully connected layer. The offline training was used to verify the method to obtain the best parameters of the model. Compared with the manual measurement results, the radius error did not exceed ±0.1mm, and the accuracy rate of sample application quality detection was 91.1%. The total detection time of a single biochip did not exceed 1.6 seconds. The proposed method can meet the requirements of accuracy and real time of product testing on the production line.