卷積神經(jīng)網(wǎng)絡(luò)模型發(fā)展及應(yīng)用

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關(guān)鍵詞：卷積神經(jīng)網(wǎng)絡(luò)；圖像識(shí)別；語義分割；自然語言處理

doi：10.3969/J.ISSN.1672-7274.2025.04.037

中圖分類號(hào)：TP183 文獻(xiàn)標(biāo)志碼：A 文章編碼：1672-7274（2025）04-0108-03

Abstract： With the rapid development of information technology，the scale and complexity of dataare constantly increasing.In this context， traditional machine learning algorithms face many chalenges when dealing with largescale data and complex tasks. Convolutional neural network models，as a deep learning algorithm，have emerged and rapidly developed.The article elaborates on the roles of the input layer，hidden layer，and output layer in the structure of convolutional neural network models.The article also reviews the development processof convolutional neural network models， which have gone through multiple stages，and introduces their practical appications in image recognition，semanticsgmentation，objecttracking，naturallanguage processing，intelligentrecommendationsstems， security monitoring，and agriculture.Convolutional neural network models playan importantrolein multiple felds due to their advantages，providing strong support for the intellgent development of various industries and having broad prospects for future development.

Keywords： convolutional neural network; image recognition; semanticsegmentation; natural language processing

當(dāng)前在人工智能領(lǐng)域，卷積神經(jīng)網(wǎng)絡(luò)模型的發(fā)展可謂突飛猛進(jìn)。（剩余4428字）