Considering the challenges of insufficient feature fusion and limited classification granularity in ElectroEncephaloGram （EEG） -based depression recognition task， a CNN-Transformer Encoder-based model for Depressive State Recognition （CTE-DSR） that integrated multidimensional EEG features was proposed. First， One-Dimensional Convolutional Neural Network （1D-CNN） was used to extract local spatial sequential features from EEG. Then， Transformer encoder module was introduced to model global correlations between leads. Simultaneously， Multi-Layer Perceptron （MLP） was used to embed depression-related non-sequential features into a high-dimensional space. Finally， the two features were fused to implement fine-grained classification of depression states through a fully connected layer. Besides， a loss weighting strategy was employed to improve the model's ability to identify minority classes. Experimental results show that compared with other five machine learning models （such as Random Forest （RF） and Adaptive Boosting （AdaBoost）） and two combination models （CNN-LSTM and CNN-RNN）， CTE-DSR achieves at least 23.0% and 6.7% higher recall and Area Under Curve （AUC） in binary classification， respectively， and at least 15.6% and 1.0% higher F1-score and macro-average AUC in ternary classification， respectively. It can be seen that the proposed model has performance advantages in identifying depressive states and can be used to assist in early screening and relapse risk assessment of depression.