The multi-scale features of time series contain abundant category information which has different importance for classification. However， the existing univariate time series classification models conventionally extract series features by convolutions with a fixed kernel size， resulting in being unable to acquire and focus on important multi-scale features effectively. In order to solve the above problem， a Multi-scale Convolution and Attention mechanism （MCA） based Long Short-Term Memory （LSTM） model （MCA-LSTM） was proposed， which was capable of concentrating and fusing important multi-scale features to achieve more accurate classification effect. In this structure， by using LSTM， the transmission of series information was controlled through memory cells and gate mechanism， and the correlation information of time series was extracted fully； by using Multi-scale Convolution Module （MCM）， the multi-scale features of the series were extracted through Convolutional Neural Networks （CNNs） with different kernel sizes； by using Attention Module （AM）， the channel information was fused to obtain the importance of features and assign attention weights， which enabled the network to focus on important time series features. Experimental results on 65 univariate time series datasets of UCR archive show that compared with the state-of-the-art time series classification methods： Unsupervised Scalable Representation Learning-FordA （USRL-FordA）， Unsupervised Scalable Representation Learning-Combined （1-Nearest Neighbor） （USRL-Combined （1-NN））， Omni-Scale Convolutional Neural Network （OS-CNN）， Inception-Time and Robust Temporal Feature Network for time series classification （RTFN），MCA-LSTM has the Mean Error （ME） reduced by 7.48， 9.92， 2.43， 2.09 and 0.82 percentage points， respectively； and achieved the highest Arithmetic Mean Rank （AMR） and Geometric Mean Rank （GMR）， which are 2.14 and 3.23 respectively. These results fully demonstrate the effectiveness of MCA-LSTM in the classification of univariate time series.