ast word segmentation, and 2 represents word segmentation. Taking the English sentence “he is read books in home” as an example, the English verb form grammar error model based on RNN algorithm is used for automatic detection. The input and output results are shown in Figure 5 . Figure 5. Open in a new tab The verb grammar error detection based on RNN language model. When the output in Figure 5 is inconsistent with the verb form “read” given by the original English sentence, the model will automatically judge the grammatical error of the original verb form “read” according to the design. 3. Experimental Evaluation of Automatic Detection of Grammatical Errors of English Verbs Based on RNN 3.1. Experimental Design of Automatic Detection of Grammatical Errors of English Verbs Based on RNN In the English language model, the learner corpus with manually labelled grammatical errors is generally used as the standard to compare the automatic detection of grammatical errors by machine learning with manually labelled grammatical errors to evaluate the effectiveness of the design or technology of automatic detection of grammatical errors by machine learning. In the whole process of comparison, it is necessary to analyze the accuracy of the two in the process of verb grammatical error detection. The accuracy calculation is shown in the following formula: Precision = ∑ i = 1 n e i ∩ g i ∑ i = 1 n e i . (13) After obtaining the accuracy of the two methods of detection, it is also necessary to compare the feedback rate of the automatic detection system. The calculation formula is as follows: Recall = ∑ i = 1 n e i ∩ g i ∑ i = 1 n g i . (14) In ( 13 ) and ( 14 ), e i is the set of machine learning automatic detection outputs of grammatical errors and g i is the set of manual markings of grammatical errors. Meanwhile, the intersection of sets e i and g i in the formula is defined as in the following formula: e i ∩ g i = e ∈ e i | ∃ g ∈ g i , match g , e . (15) Furthermore, taking th 526 cin Computational Intelligence and Neuroscience Comput Intell Neurosci Wiley PMC8541869 8541869 8541869 34697540 10.1155/2021/6052873 Automatic Detection of Grammatical Errors in English Verbs Based on RNN Algorithm: Auxiliary Objectives for Neural Error Detection Models He Yizhou 1 ✉ 1 School of Foreign Languages, Xinyu University, Xinyu, Jiangxi 338004, China Academic Editor: Bai Yuan Ding ✉ Corresponding author. 16 10 2021 2021 6052873 6052873 24 10 2021 Copyright © 2021 Yizhou He. Therefore, this paper proposes an automatic detection technology of English verb grammatical errors based on recurrent neural network (RNN) algorithm to solve this problem. Firstly, the accuracy and feedback speed of traditional manual detection and recurrent neural network RNN algorithm are compared. Secondly, a detection model which can be calculated according to grammatical order combined with context is designed. Finally, when the output verb result is inconsistent with the original text, it can automatically mark the error detection effect. In the field of writing, with the significant development of deep learning methods and machine learning algorithms, English grammar error correction (GEC) systems are striding to identify and correct grammatical errors in a full range [ 4 , 5 ], for example, the article, preposition, noun, verb, and other grammatical errors [ 6 ], such as the famous ESL assistant system designed and developed by Microsoft. From the perspective of theoretical research, generally speaking, the research methods used in GEC Researchers used these three different models for 26 types of grammatical errors in GEC system evaluation. For example, grammatical rules and n -gram grammar were used to identify verb tense and form errors, and grammatical rules and machine translation were used to identify verb missing errors. The results show that GEC system uses different models to automatically detect 26 kinds of grammatical errors, but the accuracy and recall rate of the final grammatical errors are low; for example, the recall rate of verb tense, verb form, and verb missing are 19.61%, 18.99%, and 15.19%, respectively [ 7 ]. Based on this, it is necessary to design a more optimized automatic detection method to improve the accuracy for different types of grammatical errors. As the core of English sentences, verbs are also the most complex part of speech in English learning. Therefore, this study focuses on the automatic detection technology of verb grammatical errors in English learning. In addition, at present, in the aspect of spoken language, these systems can provide specific conversation and speech situations and are further developing to support free speech situations [ 8 , 9 ]. Later, with the development of computer science and the extensive use of deep learning, they focus on feature-based engineering, such as vocabulary grammar features [ 15 ], and use machine learning for modeling to facilitate automatic detection of English grammar errors. At the same time, the research on specific grammatical errors is more extensive. For example, modern research put forward the GED system for verb forms [ 16 ]. In the research of using machine learning to realize GED, in order to improve the reliability and accuracy, different neural networks and deep learning algorithms are gradually proposed [ 17 , 18 ]. Taking the English sentence “he is read books in home” as an example, the English verb form grammar error model based on RNN algorithm is used for automatic detection. The input and output results are shown in Figure 5 . Figure 5 The verb grammar error detection based on RNN language model. When the output in Figure 5 is inconsistent with the verb form “read” given by the original English sentence, the model will automatically judge the grammatical error of the original verb form “read” according to the design. 3. Experimental Evaluation of Automatic Detection of Grammatical Errors of English Verbs Based on RNN 3.1.