In response to teachers’ insufficient knowledge management ability, this study intends to optimize the important parameters of support vector machines using lightning search algorithms to obtain improved algorithms. Research constructs a new evaluation model for teacher knowledge management ability based on improved algorithms. The performance comparison analysis of the improved algorithm proposed in the study showed that the accuracy and area under the PR curve of the algorithm were 93.47% and 0.8, superior to the comparison algorithm. In empirical analysis, using this evaluation model to improve teachers’ knowledge management ability resulted in teacher satisfaction scores of 94 points and student satisfaction scores of 96 points, both better than before the improvement. The above results indicated that the improved algorithm and teacher knowledge management ability evaluation model had good performance. Therefore, this model can be used to improve teachers’ knowledge management ability, thereby promoting the overall quality development of school teachers.
HusanaHCParamitaAS. Analysis sentiment Pada Maska Pai Pomeranian di platform Twitter Manganoan algorithm support vector machine (SVM). Teknik2021; 10(1): 18–26.
2.
HackBJDBRuiYBVivC, et al.Prediction of penetration rate by coupled simulated annealing-least square support vector machine (CSA_LSSVM) learning in a hydrocarbon formation based on drilling parameters. Energy Rep2021; 7: 3971–3978.
3.
ZhangJSunJHeH. Clustering detection method of network intrusion feature based on support vector machine and LCA block algorithm. Wirel Pers Commun2021; 3: 1–15.
4.
SongJYuCLiS. Continuous prediction of onsite PGV for earthquake early warning based on least squares support vector machine (in Chinese). Chinese Journal of Geophysics- Chinese Edition2021; 64(2): 555–568.
5.
DongQWWangSMHanFJ, et al.Innovative research and practice of teachers’ teaching quality evaluation under the guidance of ‘innovation and entrepreneurship’. Procedia Comput Sci2019; 154: 770–776.
6.
KuraniADoshiPVakhariaA, et al.A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting. Ann Data Sci2023; 10(1): 183–208.
7.
ZhangYDuY. A diagnosis model for college teachers’ teaching ability based on big data and its evaluation. Int J Emerg Technol Learn2022; 17(3): 4–17.
8.
ZhangMZhangL. Cross-cultural O2O English teaching based on AI emotion recognition and neural network algorithm. J Intell Fuzzy Syst2021; 40(4): 7183–7194.
9.
RanaPGuptaLRDubeyMK. A review on machine learning approaches in the education sector with real-time data. Journal Of Algebraic Statistics2022; 13(3): 5188–5213.
10.
WeiDRongYGargH, et al.An extended WASPAS approach for teaching quality evaluation based on pythagorean fuzzy reducible weighted Maclaurin symmetric mean. J Intell Fuzzy Syst2022; 42(4): 3121–3152.
11.
LiH. Text recognition and classification of English teaching content based on SVM. J Intell Fuzzy Syst2020; 39(5): 2–11.
12.
FangC. Intelligent online teaching system based on SVM algorithm and complex network. J Intell Fuzzy Syst2020; 40(5): 2–11.
13.
LinLLiuJZhangX, et al.Automatic translation of spoken English based on improved machine learning algorithm. J Intell Fuzzy Syst2021; 40(2): 2385–2395.
14.
LinXZamoraRBaguleyCA, et al.A hybrid short-term load forecasting approach for individual residential customer. IEEE Trans Power Deliv2022; 38(1): 26–37.
15.
AnansyWMAlqahtaniAS. An automatic facial expression recognition system employing convolutional neural network with multi-strategy gravitational search algorithm. IETE Tech Rev2022; 39(1): 72–85.
16.
KularathneSRathnayakeNHerathM, et al.Impact of economic indicators on rice production: a machine learning approach in Sri Lanka. PLoS One2024; 19(6): e0303883.
17.
PamunkeysDSRosamariaSKRahmanA. Classification of finger movements using EMG signals with PSO SVM algorithm. International Journal of Advanced Science Computing and Engineering2022; 4(3): 210–219.
18.
DaiXShengKShuF. Ship power load forecasting based on PSO-SVM. Math Biosci Eng2022; 19(5): 4547–4567.
19.
LsAJingYBXiaoJ, et al.Based on Delphi method and analytic hierarchy process to construct the evaluation index system of nursing simulation teaching quality. Nurse Educ Today2019; 79: 67–73.
20.
BaoLYuP. Evaluation method of online and offline hybrid teaching quality of physical education based on mobile edge computing. Mobile Network Appl2021; 26(5): 2188–2198.
21.
GunawanDRianaDradiancyD, et al.Kumaras algorithm support vector machine Dan Nave Bayes Degnan Algoma Geetika Pada analysis sentiment Calon Governor Jabar 2018–2023. Journal Teknik Computer2020; 6(1): 121–129.
22.
LudhianaLYoungJCRusliA. Implement Asi algorithm support vector machine dan Chi Square untuck analysis sentiment user feedback Palaksi. Journal Ultimatics2020; 12(2): 125–126.
23.
ZhanYWYangYBYuYC. A novel inverter fault-tolerant topology for faults in PMSM drive systems. Adv Ind Eng Manag2024; 13(2): 177–184.
24.
GunawanDRianaDAdrianaD, et al.Kumaras algorithm support vector machine Dan nave Bayes Degnan Altima Geetika Pada analysis sentiment Calon Governor Jabar 2018–2023. Journal Teknik Computer2020; 6(1): 121–129.
25.
LeeSKimJH. Improvement of the support vector machine-based Monte Carlo simulation for calculating failure probability. Transactions of the Korean Society of Mechanical Engineers A2020; 44(4): 269–279.
26.
KhareAAgrawalS. Scheduling hybrid flow shop with sequence-dependent setup times and due windows to minimize total weighted earliness and tardiness. Comput Ind Eng2019; 135: 780–792.
