Vertical and horizontal lines appearing on large confusion matrix?
If the $x$-axis is actual and the $y$-axis is predicted, then vertical lines means a given input of the class $x$ is not being discriminated sufficiently and as a result is randomly mapped to a number of classes. Whereas a horizontal line signifies that many classes are mapped as belonging to a particular class. 

Class imbalance

Many reasons can lead to these lines in your confusion matrix. The most common is class imbalance. If you train your model using data that is not well balanced across the different classes this will result in both horizontal and vertical lines. Consider a model trained to distinguish cats, dogs and foxes with 1000, 1000, and 10 instances each. Evidently, you can see how many future instances of foxes may be classified as dogs or cats. 

You can remedy this by obtaining more data. This is often not possible, so you can try and weight the model to account for the class imbalance. You can do this by weighting the loss function. Or by oversampling the underrepresented class. Furthermore, you can try and synthesize new instances of the underrepresented class. This can be done with a GAN (hard, requires lots of data, unstable), or you can use k-NN type techniques to find out where the data is concentrated and add novel points to add density to this set. The new technique SMOTE is also highly recommended for its simplicity.

Overlapping distributions

Another big reason is a distribution that overlaps. This is when a class' distribution encompasses one or more class distributions. For example, if you are classifying between cats, dogs, shiba inu, corgi. You can see that any amount of training will always cause conflicts between the class dog and shiba inu, corgi. Since these are dogs. 

This problem is easy to distinguish for human perceptive classification, like images, but often times we are working with data where the span of these distributions is not as obvious. You can use clustering methods to see if certain classes fall within the distribution cloud of others. I use percent overlap as a metric for this task.