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The hot spot effect is one of the main causes of damage to photovoltaic modules. In order to detect the hot spot effect of photovoltaic modules in advance, machine learning algorithms are used to optimize the design of photovoltaic module hot spot image detection methods. According to the working principle of photovoltaic modules and the mechanism of hot spot generation, set the detection standards for hot spot images of photovoltaic modules. The infrared imaging method is used to collect the hot spot image of photovoltaic module, and the initial image pre-processing is achieved through color space conversion, filtering denoising, background interference removal, image enhancement and other steps. The convolutional neural network algorithm in machine learning is used to extract the hot spot image features from both contour and color, and the detection results of the hot spot image of photovoltaic module are obtained according to the feature matching results. Through performance testing experiments, it is concluded that compared with traditional detection methods, the optimized design method has significantly lower missed detection and false detection rates, and the detection error of photovoltaic module hot spot area is lower.