Aiming at the problem of low estimation accuracy of traditional estimation methods, this paper proposes a novel state of health (SOH) estimation method based on the framework of spatio-temporal convolutional network and Pyramid Squeeze Attention Fusion (TCT-PSA) model.Convolutional Transformer-Pyramid Squeeze Attention (TCT-PSA) modeling framework for a novel state of health (SOH) estimation method. The NASA battery dataset was first preprocessed, and then the health factor (HF) was extracted from the charging stage of lithium-ion batteries, and the correlation between HF and SOH of lithium-ion batteries was quantified by using Pearson′s correlation coefficient and grey correlation analysis, and the HF with high correlation was inputted into the TCT-PSA model, and SOH was the model output. In order to verify the validity of the model, the TCT-PSA model was used to estimate the capacity degradation of each group of batteries; the SOH of each group of batteries was estimated using different models and compared, and the quantile estimation was used to verify the accuracy and robustness of the TCT-PSA model. The experimental results show that the errors of the proposed models are within 2% by validating the average absolute estimation errors of the capacity decays in the test and training sets of each group of batteries; the average absolute error (MAE), the average absolute percentage error (MAPE), and the root mean square error (RMSE) of the proposed models for estimating the SOH of each group of batteries are within 0.035; and the highest accuracy of the quartile estimation of the SOH of lithium-ion batteries is 99.82%. The highest accuracy reaches 99.82%.